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My Ph.D. Defense - Software-Defined Systems for Network-Aware Service Composition and Workflow Placement

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My Ph.D. Defense - Software-Defined Systems for Network-Aware Service Composition and Workflow Placement

  1. 1. Software-Defined Systems for Network-Aware Service Composition and Workflow Placement Pradeeban Kathiravelu Supervisors: Prof. Luís Veiga Prof. Peter Van Roy Lisboa, Portugal. July 1st , 2019 Good afternoon everyone. I am Pradeeban Kathiravelu. Today, I am presenting my Ph.D. thesis on “Software-Defined Systems for Network- Aware Service Composition and Workflow Placement." 1
  2. 2. 2/38 Introduction ● Service providers and tenants in the cloud ecosystem. – Challenges in interoperability and control. ● Network Softwarization: Management, Control, & reusability. ● Network Softwarization typically focus on a single provider. ● Network-awareness for multi-domain workflows. The cloud ecosystem consists of several service providers. Tenants, the third-party end users, consume these services rather than hosting and managing their own services on- premise. However, these providers lack interoperability among themselves. Furthermore, they provide limited control and flexibility to the tenants. These factors prevent the tenants from efficiently composing a workflow spanning multiple providers. Network softwarization makes networks programmable through software constructs, by separating the networks into network infrastructure, network control, and network services. Network Softwarization aims to resolve several challenges in network management, control, and reusability. However, network softwarization typically limits its focus to a single domain, a network managed by a single provider. A tenant workflow placement across multiple providers requires network-awareness beyond a single domain.
  3. 3. 3/38 Network Softwarization ● Software-Defined Networking (SDN) ● Network Functions Virtualization (NFV) – Network middleboxes → Virtual Network Functions (VNFs) ● Software-Defined Systems (SDS) – Storage, Security, Data center, .. – Improved configurability Software-Defined Networking and Network Functions Virtualization are two core enablers of network softwarization. SDN unifies the control of the network devices into a logically centralized controller. The controller has a global view and control of the data plane devices such as network switches and routers. It is developed in a high-level programming language such as java or python. Therefore, SDN supports efficient management of the networks. NFV, on the other hand, makes network middleboxes such as load balancer and firewall into virtual network functions and lets the users host them on servers. While SDN limits its focus to networks, Software-Defined Systems, or SDS, expands its scope to various aspects such as storage, security, and data center. SDS either extend SDN or follow an approach inspired by SDN. SDS improves the configurability of the environment by separating the mechanisms from the policies.
  4. 4. 4/38 Motivation ● Enhanced control for tenants in service workflow placements. – Tenant Policies and Service Level Objectives (SLOs) ● Address workflow challenges: technical, economic, and policy We need to bring the control of the service workflow executions back to the tenant user, despite sharing the infrastructure with several tenants. The tenants have their policies and Service Level Objectives for their workflows. The workflows should satisfy these user-defined policies and ensure quality of service to the tenant users. We aim to address the technical, economic, and policy challenges in efficiently composing and placing tenant workflows beyond the data center scale.
  5. 5. 5/38 Thesis Goals Network-Aware Service Composition and Workflow Placement Scale Intra-Domain Multi-Domain Edge The Internet The goal of this thesis is to facilitate network-aware service composition and workflow placement on environments of a varying scale: from intra-domain networks, multi-domain networks, and edge environments, to the Internet. Our contributions are at the intersection of network softwarization, service-oriented architecture, and big data. We propose to make the wide area multi- domain networks programmable, by extending SDN with SOA. We identify a set of research questions and build software-defined systems to address them. Next, we look into our research questions individually.
  6. 6. 6/38 Q1: Execution Migration Across Development Stages Can we seamlessly scale and migrate network applications through network softwarization across development and deployment stages? Scale: Data center (CoopIS’16, SDS’15, and IC2E’16) First, we look into the potential for an efficient deployment and migration of network applications and architectures across multiple execution environments, by extending network softwarization. We aim for seamless deployment and scaling of networks across the development stages such as simulations and emulations, and various deployment environments in a cluster or a data center.
  7. 7. 7/38 Q2: Economic & Performance Benefits Can network softwarization offer economic and performance benefits to the end users? Scale: Data center → Inter-cloud (Networking’18 and IM’17) Second, we look into how such a network softwarization can offer economic and performance benefits to the end users, from data centers to inter-cloud environments .
  8. 8. 8/38 Q3: Service Chain Placement Can we efficiently chain services from several edge and cloud providers to compose tenant workflows, by federating SDN deployments of the providers, using SOA? Scale: Multi-domain → Edge (ETT’18, ICWS’16, and SDS’16) Third, we look into the potential to compose tenant workflows from service instances of multiple edge and cloud providers. We federate the SDN deployments with SOA to compose tenant workflows spanning several networks, and place them in multi-domain edge environments.
  9. 9. 9/38 Q4: Interoperability Can we enhance the interoperability of diverse network applications, by leveraging network softwarization and SOA? Scale: Data center → Multi-domain and Edge (CLUSTER’18, DAPD’19, SDS’17, and CoopIS’15) Fourth, we look into the interoperability of the network application executions from the scale of data centers to multi-domain and edge environments. Can we improve the communication and coordination across the diverse distributed applications by exploiting network softwarization and SOA, and consequently, enhance their interoperability?
  10. 10. 10/38 Q5: Application to Big Data Can we improve the performance, modularity, and reusability of big data applications, by leveraging network softwarization and SOA? Scale: Data center → the Internet (CCPE’19 and SDS’18) Finally, we look into how our contributions apply to big data processing, from data centers to the Internet. Specifically, we seek to improve the performance, modularity, and reusability of big data applications by leveraging network softwarization and SOA.
  11. 11. 11/38 Thesis Contributions Q1: Seamless Development & Deployment of cloud networks Q2: Economic & Performance Benefits: Q3: Service Chain Placement: Q4: Interoperability of multi-domain service workflows Q5: Application to Big Data Cloud-Assisted Networks as an Alternative Connectivity Provider. Network Service Chain Orchestration at the Edge. Our contributions address these 5 research questions. Today we look into the 2 core contributions among these. First, the economic and performance benefits of network softwarization. Second, service chain placement at the edge.
  12. 12. 12/38 I) Cloud-Assisted Networks as an Alternative Connectivity Provider Kathiravelu, P., Chiesa, M., Marcos, P., Canini, M., Veiga, L. Moving Bits with a Fleet of Shared Virtual Routers. In IFIP Networking 2018. May 2018. pp. 370 – 378. Now we discuss our first contribution: Cloud-Assisted Networks as an alternative connectivity provider.
  13. 13. 13/38 Introduction ● Increasing demand for bandwidth. ● Decreasing bandwidth prices. ● Pricing Disparity. E.g. IP Transit price per Mbps, 2014 – USA: 0.94 $ – Kazakhstan: 15 $ – Uzbekistan: 347 $ ● What about latency? The demand for bandwidth keeps increasing. At the same time, bandwidth pricing keeps decreasing. Although this is a promising trend, there is still a significant pricing disparity between geographical regions. For example, consider the IP transit price per Mbps. As of 2014, it was less than a dollar in the USA, 15 dollars in Kazakhstan and 347 dollars in Uzbekistan. Such a disparity is not limited to the cost. The developing Internet regions rely on long-haul Internet links to the major Internet hubs to connect with the other regions. Consequently, the developing Internet regions also suffer from high latency. This state of affairs makes them inefficient for latency-sensitive web applications such as online gaming, high-frequency trading, and remote surgery.
  14. 14. 14/38 Motivation ● Dedicated connectivity* of the cloud providers. – Increasing geographical presence. – Well-provisioned network → Low latency network links. ● Cloud-Assisted Networks – Can a network overlay built over cloud instances be a better connectivity provider? ● High-performance ● Cost effectiveness * James Hamilton, VP, AWS (AWS re:invent 2016). Major cloud providers such as Amazon web services, typically manage their own global backbone network. Hence, they avoid having to route their cloud traffic, including those between their regions, through the public Internet. Their geographical presence keeps increasing with number of regions, availability zones, and points of presence. By using their own well-provisioned network exclusively, each major cloud provider manages to offer low-latency network links among their VMs. Cloud-Assisted Networks refer to overlay networks that are built on top of the cloud VMs. We ask, whether an overlay network of cloud VMs can operate as a better connectivity provider. Specifically, such a provider should provide better performance and cost-effectiveness than the current connectivity providers, such as the Internet service providers, enterprise MPLS networks, and transit providers.
  15. 15. 15/38 Our Proposal: NetUber • A Cloud-Assisted Network as a third-party virtual connectivity provider with no fixed infrastructure. – Better network paths compared to the Internet. We propose NetUber, a cloud-assisted overlay network that functions as a third-party virtual connectivity provider, with no fixed infrastructure. NetUber aims for a better control over the network path, compared to the Internet paths. Each VM in a cloud-assisted network such as NetUber functions as a virtual router and route the network traffic of the end users among each other. A cloud user can build such a cloud-assisted network on top of VMs of multiple cloud providers, and offer it as an alternative connectivity option for the end users. The end user can then use NetUber to efficiently send data between their origin server and the destination server. Each cloud region of NetUber consists of at least a broker instance. Based on the bandwidth demand from the NetUber end users and the current instance pricing, the brokers scale the NetUber overlay by purchasing more instances in their respective regions. Hence, the brokers ensure that the region has sufficient VMs for the data transfers.
  16. 16. 16/38 NetUber Application Scenarios • Cheaper data transfers between two endpoints. • Higher throughput and lower latency. • Network services. • Alternative to Software-as-a-Service replication. We identify several application scenarios for NetUber, in addition to cheaper, yet high throughput and low latency data transfers between two endpoints. NetUber could deploy network services such as compression and encryption on its cloud VMs. NetUber can then optionally execute these network services on its network flows, to improve the data transfer efficiency or as an on-demand value-added service. NetUber also provides an alternative to software-as- a-service replication. We will look into that next.
  17. 17. 17/38 NetUber Inter-Cloud Architecture • Deploy SaaS applications in one or a few regions. – Fast access from more regions with NetUber. Ohio London Belgium AWS GCP As an inter-cloud architecture, NetUber builds its overlay on top of VMs from multiple cloud providers. This architecture enables a better alternative to typical Software-as- a-Service replications across multiple cloud regions. NetUber lets us deploy the cloud applications on one or a few regions and then access them from the other regions via its cloud overlay. This approach avoids the need for the cloud user to replicate and manage their service instances in multiple regions, while still offering low-latency to their end users. Cloud providers have a few overlapping regions, and a few regions are covered by just one provider. The inter-cloud architecture enables low-latency access to all the cloud regions of the underlying cloud infrastructures. For example, Amazon Web Services, AWS, has presence in regions Ohio and London, whereas Google Cloud Platform, GCP has presence in London and Belgium. So we can build a low-latency overlay network spanning regions Ohio, London, and Belgium on top of AWS and GCP, by a direct interconnection between the VMs of both cloud providers in London. Thus, NetUber enables low-latency network connectivity between Ohio and Belgium, which would be impossible with just one of the cloud providers. Consequently, NetUber offers the end users low-latency access to more regions.
  18. 18. 18/38 Monetary Costs to Operate NetUber A.Cost of Cloud VMs (per second) – Spot instances: volatile, but up to 90% savings. B.Cost of Bandwidth (per transferred data volume). C.Cost to connect to the cloud provider (per port-hour). NetUber is a third-party service not affiliated with any cloud provider. Therefore, we must consider the operational costs of the overlay, paid to the cloud provider. A: the cost of cloud VMs. Cloud providers charge the users per second for their VM usage. This amount is still high. Therefore, NetUber uses spot instances. Spot instances are volatile, but are otherwise identical to the regular on-demand cloud instances. Using spot instances saves up to 90% of the cost to acquire the cloud instances. The AWS EC2 spot instances have fluctuating pricing with different prices across the availability zones, of any given region. Availability zones are physically separated cloud data centers from the same region that are connected by low-latency links. We cannot predict the AWS Spot instance pricing over time. NetUber acquires spot instances from the cheapest availability zone of each region at the moment and maintains the cheap ones over time. B: the cost of bandwidth. Cloud providers charge the bandwidth use per transferred data volume. This is very high, and unfortunately, there is no cheaper alternative similar to the spot instances. C: the cost to connect the end user’s on-premise server to the cloud. Typically the end user pays to the cloud provider directly to connect their on-premise servers to the cloud via a Direct connect. The cloud providers charge the end user per port-hour for the Direct Connect – for example, how many hours a 10 Gbps Ethernet port is used. The NetUber end user must incur lower total cost compared to what they spend for their existing connectivity providers, with better performance, to consider NetUber economically viable.
  19. 19. 19/38 Evaluation • NetUber prototype with AWS r4.8xlarge spot instances. • Cheaper point-to-point connectivity. ● Better throughput and reduced latency & jitter. – Origin: RIPE Atlas Probes and our distributed servers. – Destination: VMs of multiple AWS regions. ● Network Services: Compression We evaluate the cost and performance of NetUber. We use AWS as the cloud provider for our evaluations. We use r4.8xlarge memory-optimized spot instances, each with 10 Gbps network interface to build our NetUber cloud overlay prototype. We benchmark NetUber against 2 enterprise connectivity providers for its cost-effectiveness. We then benchmark NetUber against ISPs for latency, throughput, and jitter. We send data from RIPE Atlas Probes and our distributed servers, towards the AWS spot instances from multiple regions. The RIPE Atlas gives us access to physical nodes across the Internet. We also evaluate the potential for network services – specifically, compression.
  20. 20. 20/38 1) Cheaper Point-to-Point Connectivity • Cost for 10 Gbps flat connectivity: from EU & USA. – Cheaper for data transfers <50 TB/month. First, we benchmark the cost for 10 Gbps flat connectivity for data transfers from the EU and the USA. We benchmark NetUber regular deployment, and a deployment with a 75% compression on data transfers, against two connectivity providers. The provider 1 uses an overlay on its large global infrastructure to provide connectivity - A basic connectivity and a more expensive premium one that provides faster internet routes by interconnecting with premium networks. The provider 2 is a transit provider. We observe that NetUber is cheaper for data transfers up to 50 terabytes per month, compared to the considered 2 connectivity providers for the same regions. With data compression on the network flows, NetUber can remain cheaper for larger volumes of data.
  21. 21. 21/38 2) Low Latency with Cloud Routes • NetUber data transfer A → Z via the path A → B → Z. – Cloud region B is closer to the origin server A. – B and Z are cloud VMs connected by NetUber overlay. Next, we benchmark the latency of NetUber against the ISP-based Internet paths for data transfer between two endpoints. NetUber relies on the nearest cloud region to route its traffic through. In this sample scenario, cloud region B is closer to A, and B is connected to the cloud region Z by the Netuber overlay. Here when we send data from A to Z using NetUber, we send the data from A to the cloud region B first via ISP, and then from B to Z using the NetUber cloud overlay. We compare the latency of this NetUber data transfer against sending data from A to Z directly using the public Internet, with the ISP network connectivity. In this example, we have Vladivostok as the origin, and Sao Paolo as the destination region. Tokyo is the nearest cloud region to the origin.
  22. 22. 22/38 Ping times – ISP vs. NetUber (via region, % Improvement) • NetUber cuts Internet latencies up to 30%. • Direct Connect would make NetUber even faster. We evaluated the latency of data transfers between several pairs of origin and destination. This table lists the ping time latencies via ISP-based public Internet, and NetUber – together with the cloud region which NetUber uses to route its traffic through for each transfer, as well as the percentage reduction in latency with NetUber. We observe that NetUber cuts the internet latencies up to a factor of 30%. We highlight that the use of Direct connect would make NetUber even faster.
  23. 23. 23/38 3) Throughput: ISP, NetUber, and Selectively Using NetUber ● Better throughput with NetUber via near cloud region. – Selective use of overlay when no proximate region. We then benchmark the NetUber throughput against the ISP- based public Internet. We first connect our origin server in Atlanta to the NetUber overlay via ISP. Our nearest cloud region is North Virginia. We observe that sending data with NetUber via the nearest cloud region can be more stable and offer high throughput, rather than sending the data to the destination via the public Internet paths. NetUber avoids slow long-haul Internet links as it covers a significant portion of the data transfer network path. We then repeat the experiment across multiple regions of origin and destination. Using a cloud overlay may not always provide better throughput, especially if there is no cloud region near to the origin or destination. As shown in this case, the end-user device can be configured to use the NetUber overlay selectively, using it only when it provides a better performance, and using the public Internet paths otherwise.
  24. 24. 24/38 4) Low Jitter with Cloud Overlay ● NetUber for latency-sensitive web applications. We finally benchmark the jitter of NetUber against that of ISP as latency variations. In case of NetUber, we connect the origin and destination endpoints with the cloud overlay using 2 approaches – through the ISP-based public Internet, and through a simulated Direct Connect, modeled with realistic latency values. We observe minimal latency variations with NetUber. We note that latency variation in NetUber in most cases is due to the variations in the ISP’s network connecting the user endpoint servers with the nearest cloud region. The cloud Direct Connects promise a fixed dedicated connectivity to the end users to connect their on-premise servers to the cloud. Therefore, latency variations are negligible in the Cloud Direct Connects. Consequently, latency variations in NetUber with a direct connect represent the actual jitter caused by the NetUber overlay. The minimal jitter observed in NetUber highlights its suitability for latency- sensitive web applications.
  25. 25. 25/38 Key Findings • Connectivity provider that does not own the infrastructure – Low latency cloud-assisted overlay network. – Better data rate than ISPs. • Previous research do not consider economic aspects. – A cheaper alternative (< 50 TB/month). • Similar industrial efforts. – Voxility, an alternative to transit providers. – Teridion, Internet fast lanes for SaaS providers. Finally, to summarize: NetUber is a connectivity provider that does not own the infrastructure. NetUber offers low latency end-to-end data transfer through its cloud-assisted network. We observed up to 30% reduction in latency, even without using the Direct Connects. We observe that the ISPs typically limit their data rate to 100 Mbps, that too often with a cap. NetUber can provide a better data rate for end users compared to the ISPs. Previous research works on cloud-assisted networks do not consider economic aspects. We looked in detail on the economics of using a cloud-assisted network as a connectivity provider. NetUber is cheaper than the considered connectivity providers for up to 50 Terabytes per month. There are a few companies that follow an approach similar to NetUber. Voxility operates as an alternative to transit providers using an overlay network built on top of its global infrastructure. Teridion offers internet fast lanes for Software-as-a-Service providers. We conclude that cloud-assisted networks are growing popularity in research and enterprise, and NetUber provides a first look into its potential as a connectivity provider, both from technological and economic perspectives.
  26. 26. 26/38 II) Network Service Chain Orchestration at the Edge Kathiravelu, P., Van Roy, P., & Veiga, L. Composing Network Service Chains at the Edge: A Resilient and Adaptive Software- Defined Approach. In Transactions on Emerging Telecommunications Technologies (ETT). Aug. 2018. Wiley. pp. 1 – 22. Now we discuss our second contribution: Network Service Chain Orchestration at the Edge
  27. 27. 27/38 Motivation ● Network Services: On-Premise vs. Centralized Cloud? Edge! ● Network Service Chaining (NSC) ● Finding optimal service chain at the edge abiding by the tenant SLOs. Cloud environments mitigate the resource scarcity on-premise to execute complex user workflows. However, centralized clouds suffer from high latency. Edge environments provide a balance – low latency with sufficient resources. Therefore, more and more service providers choose to deploy their network services at the edge of the network, close to their users. Network service chaining refers to a workflow of network services chained together, with the output of one or more services sent as the input to the next services in the chain. Consider this sample service chain: The Internet traffic reaches the user through a chain of network services – video optimizer, cache, anti-virus, and finally the firewall. But when it comes to a child accessing the Internet, we have a slightly different workflow – The traffic goes through parental control first before reaching the other services and then the child. Selecting the optimal service instances to compose service workflows at the edge is challenging due to the volume and variety of the service instances and the number of tenant users. We should find the optimal service chain for the user workflow, abiding by its service level objectives. Such service chain placement is considered to be an NP-Hard problem. Geographical proximity is a deciding factor in service deployment at the edge. But consider this sample service chain: the edge nodes n1 and n2 are close to the user. However, the related services next in line in the service chain are not available in the same nodes. Therefore, choosing n1 and n2 to host the service workflow leads to more inter-node data flow, and consequently high latency. On the other hand, although n3 and n4 are farther from the user, n4 consists of 3 related services in the workflow. Therefore, choosing n3 and n4 reduces the inter-node communication overheads. These are additional constraints specific to the service workflows that do not apply to stand-alone service executions.
  28. 28. 28/38 Our Proposal: Évora ● Graph-based algorithm to incrementally construct user workflows as service chains at the edge. ● SDN With Message-Oriented Middleware (MOM). – For multi-domain edge environments. – Place and migrate user service chains. ● Adhering to the user policies. We propose Evora, a graph-based algorithm to incrementally construct and deploy user workflows as service chains at the edge. Evora architecture extends SDN to multi-domain edge environments with message-oriented middleware. It enables placing and migrating user service chains, adhering to the user-defined policies.
  29. 29. 29/38 Deployment Architecture ● Distributed execution: Orchestrator in each user device. In the Evora deployment, each user device consists of an orchestrator. The orchestrator executes the Evora algorithms to place and migrate service chains, in a decentralized and distributed manner. A few edge nodes are equipped with an SDN controller, extended with a message broker. The controller centrally manages its network domain, while communicating and coordinating with the other controllers at the edge. Each user device and edge node consists of an Event Manager. The Event Manager publishes the status of the node and the respective services to the broker as event notifications. It also receives the relevant status details of the edge nodes and services, from the broker. Therefore it functions as both an event publisher and an event subscriber. The black lines indicate static network links across the edge nodes. The dotted red lines indicate dynamic links among the edge nodes as well as between the edge nodes and the user devices. These dynamic links are enabled by messages through the public Internet .
  30. 30. 30/38 Évora Orchestration 1) Initialize Orchestrator in each Device ● Construct a service graph in the user device. ― As a snapshot of the service instances at the edge. Evora orchestration consists of 3 major steps. First, a one-time initialization of the Orchestrator for each user device. During the initialization, the orchestrator constructs a service graph in the device, as a snapshot of the available service instances at the edge.
  31. 31. 31/38 2) Identify Potential Workflow Placements ● Construct potential chains incrementally. – Subgraphs from service graph to match user chain. – Noting individual service properties. ● A complete match? – Save as a potential service chain placement. Then, identifying the potential workflow placements for each user service chain. The orchestrator traverses the service graph and incrementally identifies potential workflow placements by matching its subgraphs against the user- defined service chain. The orchestrator also notes each service properties such as monetary cost, throughput, and end-to-end latency for the potential chains that it constructs. Subgraphs of the service graph that completely match the user service chain, are identified as the potential candidates for the service chain placement and saved in the user device. The algorithm halts its execution once it has completely traversed all the service graph nodes. Subsequent executions of the same workflow require no initialization.
  32. 32. 32/38 3) Service Chain Placement ● Calculate a penalty value for potential placements. – Normalized values: Cost, Latency, and Throughput. – α,β,γ ← User-specified weights. ● Place NSC on composition with minimal penalty value. – Mixed Integer Linear Problem. – Extensible with powers and more properties. The orchestrator computes a penalty value for each potential chain, using normalized values for the service properties and the user-assigned weights to the properties, It then places the user service workflow on the service composition with minimal penalty value among the potential service compositions. The workflow placement is solved by a mixed integer linear problem. We can extend the workflow placement with more properties and powers. Evora also migrates the workflows upon changes in the edge nodes or the services. When a service becomes unavailable or unresponsive, the orchestrator chooses the next potential service composition for the affected workflow, and schedules the subsequent requests to the workflow accordingly.
  33. 33. 33/38 Evaluation ● Model sample edge environment. – Service nodes and a user device. – User policies for the service workflow. ● Microbenchmark Évora workflow placement. – Effectiveness in satisfying user policies. – Efficacy in closeness to optimal results ● ↡Penalty value ➡ ↟Quality of experience We model an edge environment with service nodes and a user device. The user composes service workflows with their policies and uses Evora orchestrator to find optimal workflow placement at the edge. We evaluate the effectiveness of Evora in satisfying those user policies in workflow placement. We also assess the efficacy in closeness to optimal results. Workflow placements with minimized penalty values should offer the user a high quality of experience.
  34. 34. 34/38 User Policies with Two Properties ● Equal weights to 2 properties among C, L, and T. ● Darker circles – compositions with minimal penalty. – The ones that Évora chooses (circled). T ↑ and C ↓ T ↑ and L ↓ C ↓ and L ↓ We first evaluate Evora with user policies consisting of two properties among cost, end-to-end latency, and throughput – with equal weights. The location of the circles in the plots indicate the values of the properties among potential service chains. Darker circles indicate the chains with minimal penalty values, the ones the user prefers. The chains that Evora chooses are indicated by the pink circles in these plots. First, the user defines her policies, preferring high throughput and low cost. As we observe, the darkest circles were indeed among the high throughput and relatively lower cost – a trade-off, considering both properties. The second one successfully chose the service compositions with both highest throughput with lowest latency. The third one indeed chose the ones with lowest cost and lowest latency.
  35. 35. 35/38 Policies with Three Properties: One given more prominence (weight = 10), than the other two (weight = 3). Radius of the circles – Monthly Cost Next, we evaluate Evora with all three properties – but one of the properties is given prominence with a weight of 10 while the other 2 have a weight of 3. The radius of the circles indicates the monthly cost in these plots, with the x-axis representing throughput and y-axis representing latency. First, we maximize throughput. The far right shows the darkest circle, as desired – choosing the chain placements with the highest throughput. Evora also has chosen the composition with low latency. Second, we minimize the cost. We notice that Evora has chosen the composition with the lowest cost and also the lowest latency. However, as the priority was given to cost, we note that the chosen service compositions suffer from low throughput. Third, we minimize latency. Here we observe Evora has chosen the compositions with lowest latency and highest throughput.
  36. 36. 36/38 Two given more prominence (weight = 10), than the third (weight = 3). ● Effectively satisfying the user policies – multiple properties with different weights. We repeated the experiment, this time giving more prominence to 2 properties equally among the 3. First, maximize throughput and minimize cost. We note that the compositions with high throughput have been chosen. We also note the preference for the cheaper service chains as can be seen from the dark smaller circles. Then we maximize throughput and minimize latency. We note, the dark circles are in the bottom right, correctly choosing the workflow placements with the highest throughput and the lowest latency. Finally, we minimize both cost and latency. We observe, the smallest circles in the bottom left have been chosen. Here we observe that while minimizing both cost and latency, Evora has chosen compositions with lower throughput. This is a trade-off we had to make due to the higher cost of the high throughput service instances. From the position of the dark circles, we observe that Evora adequately satisfies the user policies in the service chain placements.
  37. 37. 37/38 Key Findings ● Bring control back to the users for edge workflows. ● Previous research focus on single NSC provider. ● Évora efficient workflow placement. – Abiding by the user policies. – Multi-domain edge with multiple providers. – Extending SDN with MOM to wide area networks. ● Network-aware execution from user devices. – Decentralized and distributed. Finally, to summarize: We should bring the control of the user workflows back to the users, to efficiently compose workflows using network services from multiple service providers at the edge. Previous works mostly focus on a single provider for the entire workflow execution. Evora proposes an effiient workflow placement for the multi-domain edge environments with multiple providers, abiding by the user policies. Evora extends SDN with message-oriented middleware to wide area networks. Evora executes its network- aware workflow placement algorithms from the user devices in a decentralized and distributed manner.
  38. 38. 38/38 Conclusion ● Seamless migration across development and deployments. ● A case for Cloud-Assisted Networks as a connectivity provider. ● Composing & placing workflows in multi-domain networks. ● Increased interoperability with network softwarization & SOA. ● Applicability of our contributions in the context of Big Data. Future Work ● NetUber as an enterprise connectivity provider. ● Adaptive network service chains on hybrid networks. Thank you! Questions? In this dissertation, we proposed a set of Software-Defined Systems to address several shortcomings in the current services ecosystem. First, we enabled a seamless migration of network algorithms and architectures across development stages and deployment environments. Second, we demostrated cloud-assisted networks as a cost- efficient and high-performant connectivity provider. Third, we composed and placed user workflows in multi-domain networks with network softwarization and SOA. Fourth, we highlighted how our network softwarization approach, extended with SOA, increases the interoperability in the services ecosystem. Finally, we discussed the applicability of our contributions in the context of big data. As future work, we propose to deploy NetUber on more cloud providers and evaluate on more regions. We also propose to look into the feasibilities of NetUber as an enterprise connectivity provider, in practice – including the challenges and opportunities. Further, we also propose to research adaptive network service chains on hybrid networks with hardware middleboxes in addition to VNFs. As NFV is still not widely adopted on several enterprise networks, supporting hybrid networks will enable service compositions at Internet scale, with several service and infrastructure providers. Thank you for your attention, and now I open the session for questions.

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