Performance Comparision of Dynamic Load Balancing Algorithm in Cloud ComputingEswar Publications
Cloud computing as a distributed paradigm, it has the latent to make over a large part of the Cooperative industry. In cloud computing it’s automatically describe more technologies like distributed computing, virtualization, software, web services and networking. We review the new cloud computing technologies, and indicate the main challenges for their development in future, among which load balancing problem stands out and attracts our attention Concept of load balancing in networking and in cloud environment both are widely different. Load balancing in networking its complete concern to avoid the problem of overloading and under loading in any sever networking cloud computing its complete different its involves different elements metrics such as security, reliability, throughput, tolerance, on demand services, cost etc. Through these elements we avoiding various node problem of distributing system where many services waiting for request and others are heavily loaded and through these its increase response time and degraded performance optimization. In this paper first we classify algorithms in static and dynamic. Then we analyzed the dynamic algorithms applied in dynamics environments in cloud. Through this paper we have been show compression of various dynamics algorithm in which we include honey bee algorithm, throttled algorithm, Biased random algorithm with different elements and describe how and which is best in cloud environment with different metrics mainly used elements are performance, resource utilization and minimum cost. Our main focus of paper is in the analyze various load
balancing algorithms and their applicability in cloud environment.
The Scheduler.
What if two tasks have the same priority are ready?
Task object data.
System tasks.
Hello World application using RTOS.
References and Read more
Performance Comparision of Dynamic Load Balancing Algorithm in Cloud ComputingEswar Publications
Cloud computing as a distributed paradigm, it has the latent to make over a large part of the Cooperative industry. In cloud computing it’s automatically describe more technologies like distributed computing, virtualization, software, web services and networking. We review the new cloud computing technologies, and indicate the main challenges for their development in future, among which load balancing problem stands out and attracts our attention Concept of load balancing in networking and in cloud environment both are widely different. Load balancing in networking its complete concern to avoid the problem of overloading and under loading in any sever networking cloud computing its complete different its involves different elements metrics such as security, reliability, throughput, tolerance, on demand services, cost etc. Through these elements we avoiding various node problem of distributing system where many services waiting for request and others are heavily loaded and through these its increase response time and degraded performance optimization. In this paper first we classify algorithms in static and dynamic. Then we analyzed the dynamic algorithms applied in dynamics environments in cloud. Through this paper we have been show compression of various dynamics algorithm in which we include honey bee algorithm, throttled algorithm, Biased random algorithm with different elements and describe how and which is best in cloud environment with different metrics mainly used elements are performance, resource utilization and minimum cost. Our main focus of paper is in the analyze various load
balancing algorithms and their applicability in cloud environment.
The Scheduler.
What if two tasks have the same priority are ready?
Task object data.
System tasks.
Hello World application using RTOS.
References and Read more
Load balancing In cloud - In a semi distributed systemAchal Gupta
Load Balancing in Cloud
What is load balancing in Cloud in semi distributed system and why it is better than a centralized system and distributed system
Linked In Stream Processing Meetup - Apache PulsarKarthik Ramasamy
Apache Pulsar is the next generation messaging system that uses a fundamentally different architecture to achieve durability, performance, scalability, efficiency, multi-tenancy and geo replication.
Building your own Distributed System The easy way - Cassandra Summit EU 2014Kévin LOVATO
Although Cassandra is well known for its ability to scale and handle heavy load, the team at Abc Arbitrage has preferred to expose its capacity to act as a distributed system.
In this presentation, Kévin Lovato, Software Engineer, will focus on the creation of their home-made Service Bus's Directory which relies on Cassandra to behave as a full-fledged distributed system.
IT IS ABOUT MULTIPROCESSING,COMMUNICATION BETWEEN THE PROCESS THROUGH MESSAGE PASSING AND SHARED MEMORY.SYNCHRONIZATION MECHANISM AND SYNCHRONIZATION USING SEMAPHORE
A tutorial-like technical presentation that covers fundamental approaches for replication along with their advantages, disadvantages, comparisons with each other etc.
Base paper ppt-. A load balancing model based on cloud partitioning for the ...Lavanya Vigrahala
A load balancing model based on cloud partitioning for the public cloud. -Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
Observer, a "real life" time series applicationKévin LOVATO
Time series examples are often seen in the Cassandra literature, but how do we deal with them in real life applications, outside of the usual "weather station" example?
We have been building and perfecting our own metrics system for over a year and we will share what we've learned, from schema design to data access optimization.
Seastore: Next Generation Backing Store for CephScyllaDB
Ceph is an open source distributed file system addressing file, block, and object storage use cases. Next generation storage devices require a change in strategy, so the community has been developing crimson-osd, an eventual replacement for ceph-osd intended to minimize cpu overhead and improve throughput and latency. Seastore is a new backing store for crimson-osd targeted at emerging storage technologies including persistent memory and ZNS devices.
A load balancing model based on cloud partitioning for the public cloud. ppt Lavanya Vigrahala
Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
Dominant block guided optimal cache size estimation to maximize ipc of embedd...ijesajournal
Embedded system software is highly constrained from performance, memory footprint, energy consumption and implementing cost view point. It is always desirable to obtain better Instructions per Cycle (IPC). Instruction cache has major contribution in improving IPC. Cache memories are realized on the same chip where the processor is running. This considerably increases the system cost as well. Hence, it is required to maintain a trade-off between cache sizes and performance improvement offered. Determining the number of cache lines and size of cache line are important parameters for cache designing. The design space for cache is quite large. It is time taking to execute the given application with different cache sizes on an instruction set simulator (ISS) to figure out the optimal cache size. In this paper, a technique is proposed to identify a number of cache lines and cache line size for the L1 instruction cache that will offer best or nearly best IPC. Cache size is derived, at a higher abstraction level, from basic block analysis in the Low Level Virtual Machine (LLVM) environment. The cache size estimated from the LLVM environment is cross validated by simulating the set of benchmark applications with different cache sizes in SimpleScalar’s out-of-order simulator. The proposed method seems to be superior in terms of estimation accuracy and/or estimation time as compared to the existing methods for estimation of optimal cache size parameters (cache line size, number of cache lines).
Load balancing In cloud - In a semi distributed systemAchal Gupta
Load Balancing in Cloud
What is load balancing in Cloud in semi distributed system and why it is better than a centralized system and distributed system
Linked In Stream Processing Meetup - Apache PulsarKarthik Ramasamy
Apache Pulsar is the next generation messaging system that uses a fundamentally different architecture to achieve durability, performance, scalability, efficiency, multi-tenancy and geo replication.
Building your own Distributed System The easy way - Cassandra Summit EU 2014Kévin LOVATO
Although Cassandra is well known for its ability to scale and handle heavy load, the team at Abc Arbitrage has preferred to expose its capacity to act as a distributed system.
In this presentation, Kévin Lovato, Software Engineer, will focus on the creation of their home-made Service Bus's Directory which relies on Cassandra to behave as a full-fledged distributed system.
IT IS ABOUT MULTIPROCESSING,COMMUNICATION BETWEEN THE PROCESS THROUGH MESSAGE PASSING AND SHARED MEMORY.SYNCHRONIZATION MECHANISM AND SYNCHRONIZATION USING SEMAPHORE
A tutorial-like technical presentation that covers fundamental approaches for replication along with their advantages, disadvantages, comparisons with each other etc.
Base paper ppt-. A load balancing model based on cloud partitioning for the ...Lavanya Vigrahala
A load balancing model based on cloud partitioning for the public cloud. -Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
Observer, a "real life" time series applicationKévin LOVATO
Time series examples are often seen in the Cassandra literature, but how do we deal with them in real life applications, outside of the usual "weather station" example?
We have been building and perfecting our own metrics system for over a year and we will share what we've learned, from schema design to data access optimization.
Seastore: Next Generation Backing Store for CephScyllaDB
Ceph is an open source distributed file system addressing file, block, and object storage use cases. Next generation storage devices require a change in strategy, so the community has been developing crimson-osd, an eventual replacement for ceph-osd intended to minimize cpu overhead and improve throughput and latency. Seastore is a new backing store for crimson-osd targeted at emerging storage technologies including persistent memory and ZNS devices.
A load balancing model based on cloud partitioning for the public cloud. ppt Lavanya Vigrahala
Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
Dominant block guided optimal cache size estimation to maximize ipc of embedd...ijesajournal
Embedded system software is highly constrained from performance, memory footprint, energy consumption and implementing cost view point. It is always desirable to obtain better Instructions per Cycle (IPC). Instruction cache has major contribution in improving IPC. Cache memories are realized on the same chip where the processor is running. This considerably increases the system cost as well. Hence, it is required to maintain a trade-off between cache sizes and performance improvement offered. Determining the number of cache lines and size of cache line are important parameters for cache designing. The design space for cache is quite large. It is time taking to execute the given application with different cache sizes on an instruction set simulator (ISS) to figure out the optimal cache size. In this paper, a technique is proposed to identify a number of cache lines and cache line size for the L1 instruction cache that will offer best or nearly best IPC. Cache size is derived, at a higher abstraction level, from basic block analysis in the Low Level Virtual Machine (LLVM) environment. The cache size estimated from the LLVM environment is cross validated by simulating the set of benchmark applications with different cache sizes in SimpleScalar’s out-of-order simulator. The proposed method seems to be superior in terms of estimation accuracy and/or estimation time as compared to the existing methods for estimation of optimal cache size parameters (cache line size, number of cache lines).
Dominant block guided optimal cache size estimation to maximize ipc of embedd...ijesajournal
Embedded system software is highly constrained from performance, memory footprint, energy consumption
and implementing cost view point. It is always desirable to obtain better Instructions per Cycle (IPC).
Instruction cache has major contribu
tion in improving IPC. Cache memories are realized on the same chip
where the processor is running. This considerably increases the system cost as well. Hence, it is required to
maintain a trade
-
off between cache sizes and performance improvement offered.
Determining the number
of cache lines and size of cache line are important parameters for cache designing. The design space for
cache is quite large. It is time taking to execute the given application with different cache sizes on an
instruction set simula
tor (ISS) to figure out the optimal cache size. In this paper, a technique is proposed to
identify a number of cache lines and cache line size for the L1 instruction cache that will offer best or
nearly best IPC. Cache size is derived, at a higher abstract
ion level, from basic block analysis in the Low
Level Virtual Machine (LLVM) environment. The cache size estimated from the LLVM environment is cross
validated by simulating the set of benchmark applications with different cache sizes in SimpleScalar’s out
-
of
-
order simulator. The proposed method seems to be superior in terms of estimation accuracy and/or
estimation time as compared to the existing methods for estimation of optimal cache size parameters (cache
line size, number of cache lines).
Optimizing your java applications for multi core hardwareIndicThreads
Session Presented at 5th IndicThreads.com Conference On Java held on 10-11 December 2010 in Pune, India
WEB: http://J10.IndicThreads.com
------------
Rising power dissipation in microprocessor chips is leading to a trend towards increasing the number of cores on a chip (multi-core processors) rather than increasing clock frequency as the primary basis for increasing system performance. Consequently the number of threads in commodity hardware has also exploded. This leads to complexity in designing and configuring high performance Java applications that make effective use of new hardware. In this talk we provide a summary of the changes happening in the multi-core world and subsequently discuss about some of the JVM features which exploit the multi-core capabilities of the underlying hardware. We also explain techniques to analyze and optimize your application for highly concurrent systems. Key topics include an overview of Java Virtual Machine features & configuration, ways to correctly leverage java.util.concurrent package to achieve enhanced parallelism for applications in a multi-core environment, operating system issues, virtualization, Java code optimizations and useful profiling tools and techniques.
Takeaways for the Audience
Attendees will leave with a better understanding of the new multi-core world, understanding of Java Virtual Machine features which exploit mulit-core and the techniques they can apply to ensure their Java applications run well in mulit-core environment.
WRITE BUFFER PARTITIONING WITH RENAME REGISTER CAPPING IN MULTITHREADED CORESijdpsjournal
In simultaneous multithreaded systems, there are several pipeline resources that are shared amongst multiple
threads concurrently. Some of these mutual resources to mention are the register-file and the write buffer.
The Physical Register file is a critical shared resource in these types of systems due to the limited number of
rename registers available for renaming. The write buffer, another shared resource, is also crucial since it
serves as an intermediary between the retirement of a store instruction and the writing of its value to cache.
Both components, if not configured accurately, can serve as a bottleneck in inefficient usage of the resources
and output undesirable performance.
However, when configuring both shared components concurrently, there is potential to all eviate common
performance congestion. This paper shows that when implementing a static register capping algorithm
(limiting the number of physical register entries for each thread), there is a byproduct of increased variety
in source for the write buffer. This also presents an opportunity for the write buffer to have a higher variety
to potentially choose for a better suitable thread asit’s source at certain clock cycles. With this presented
opportunity, this paper proposes a technique to allow the write buffer to both prioritize and enforce the
choice for low-latency threads by partitioning the write buffer in two sections; cache-hit priority and cachehit only partitions, showing that system performance and resource efficiency can be further improved by
using this technique in a modified SMT environment.
An Efficient Low Complexity Low Latency Architecture for Matching of Data Enc...IJERA Editor
An efficient architecture is introduced for the matching of data encoded with error correcting code using a cache memory is presented in brief. Using cache memory it reduces latency and complexity to an fine level. And this architecture further reduces the dynamic power without affecting the time. For the comparison of data, hamming distance along used to check whether the data match the data kept in main memory. Instead of butterfly formed weight accumulator(previous work) here no other mechanism is presented for calculating hamming distance.
[ACNA2022] Hadoop Vectored IO_ your data just got faster!.pdfMukundThakur22
Since 2006 the world of big data has moved from terabytes to hundreds of petabytes, from local clusters to remote cloud storage, yet the original Apache Hadoop POSIX-based file APIs have barely changed.
It is wonderful that these APIs have worked so well, but we can do a lot better with remote object stores, by providing new operations which suit them better, targeted at columnar data libraries such as ORC and Spark. Only a few libraries need to migrate to these APIs for significant speedups of all big data applications.
This talk introduces a new Hadoop Filesystem API called "vectored read", coming in Hadoop 3.4. An extension of the classic FSDataInputStream it is automatically offered by all filesystem clients.
The S3A connector is the first object store to provide a custom implementation, reading different blocks of data in parallel. In Apache Hive benchmarks with a modified ORC library, we saw a 2x speedup compared to using the classic s3a connector through the Posix APIs.
We will introduce the API spec, the S3A implementation, and the benchmarks, and show how to use it in your own applications. We will also cover our ongoing work on providing similar speedups with other object stores, and the use of the API in other applications.
Operating System
Topic Memory Management
for Btech/Bsc (C.S)/BCA...
Memory management is the functionality of an operating system which handles or manages primary memory. Memory management keeps track of each and every memory location either it is allocated to some process or it is free. It checks how much memory is to be allocated to processes. It decides which process will get memory at what time. It tracks whenever some memory gets freed or unallocated and correspondingly it updates the status.
1. Adaptive Write-Back Destaging for Server-Side Caches
Gokul Nadathur, Venkatesh Kothakota, Chethan Kumar,
Mahesh Patil, Karthik Dwarakinath, Vanish Talwar
PernixData, Inc.
Problem Statement: We consider a virtualized server
side storage acceleration architecture wherein IOs from
each host are accelerated by a local cache in write back
mode [1]. Each host is connected to a variety of NAS
and SAN storage backends with different performance
capabilities. Writing dirty data to backend storage is
done by the destager by batching a fixed number of IOs.
Since reads are mostly cached, this fundamentally alters
the workload to the storage from mixed read writes to a
purely bursty write biased workload of varying IO sizes.
Maintaining a constant batch size in the destager that is
agnostic of the actual capability of the storage backend
results in sub-optimal write throughput accompanied by
high latencies. The problem is further compounded when
multiple destagers in the cluster write to the same shared
storage. Finally, every destager IO also involves reading
from an acceleration tier the behavior of which should
also be factored.
Adaptive Destager: We address these problems by
designing an adaptive destager that optimizes destager
write throughput and latency in a diverse storage envi-
ronment. The scheme also works with multiple destagers
in the system without any explicit state sharing. Specifi-
cally, we introduce an adaptation module to the destager
consisting of (i) a monitoring component to collect
throughput and latency stats of the flash device and back-
end storage. These are fed into (ii) an adaptor component
that applies a learning algorithm to dynamically pick the
optimal batch size of the IOs issued by the destager. This
optimal size is then enforced by (iii) a controller that ac-
tuates the batch size in the overall destager workflow.
The adaptor’s learning algorithm robustly quantifies and
detects the optimal write throughput (OWT) available to
a destager in the cluster. It runs continuously and dy-
namically updates the OWT to respond to changing IO
conditions in the cluster. We calculate OWT in 2 ways
- (i) as the knee point of batch size vs observed write
throughput curve [2], and (ii) as the knee point of write
batch size vs observed write throughput power curve,
where throughput power is the ratio of throughput to
latency [3]. The latency is aggregated across accelera-
tion and storage tier accommodating negative changes in
both tiers. The batch size at OWT is used as the optimal
batch size for the destager. The destager starts with a
conservative value for the write batch size which is then
increased by the adaptation module at regular intervals
based on OWT calculation and checks to see if the knee
has been reached. Once a knee is determined, the sys-
tem stays at this point until certain counter conditions
are triggered. When a counter condition is hit, the system
backs off from the current knee and the whole detection
process is repeated again. Every destager in the cluster
executes the above algorithm. Since the changing con-
ditions of the shared storage such as throughput loss or
latency increase is visible to all the destagers, the differ-
ent destagers in the cluster reach an equilibrium without
explicit state sharing. Our results so far show upto 30%
predictable improvement in throughput and 50% reduc-
tion in latency under overloaded conditions.
Related Work: PARDA [4] enforces proportional-share
fairness among distributed hosts accessing a storage de-
vice at a latency threshold using flow control mecha-
nisms similar to FAST TCP. PARDA operates at the
VM level and is not designed to optimize aggregate IO
throughput. Our work achieves the best possible bal-
ance between storage backend throughput and latency in
a stateless algorithm with no user intervention. Similarly,
while some of the techniques from other adaptation pa-
pers may be applicable, the overall architecture and con-
straints are different.
[1] BHAGWAT D., et. al. A practical implementation of clustered fault
tolerant write acceleration in a virtualized environment. In FAST 2015.
[2] SATOPAA V., et. al. Finding a kneedle in a haystack: Detecting
knee points in system behavior. In ICDCSW 2011.
[3] KLEINROCK L. Power and deterministic rules of thumb for prob-
abilistic problems in computer communications. In International Con-
ference on Communications 1979.
[4] GULATI A., et. al. PARDA: proportional allocation of resources
for distributed storage access. In FAST 2009.