Using the SLALOM model to improve Cloud SLAs
Efstathios Karanastasis
ICCS/NTUA
SLALOM Technical Track
2
Problem snapshot
SLA Technological Landscape
• A lot of ambiguities exist in SLAs of Cloud providers
• The measurement/aud...
Problem snapshot
Ambiguities in SLAs
• Availability (as defined by providers) definition may encapsulate different
formula...
Problem snapshot
Real world example of Ambiguity
• Ambiguity in the measurement process of AWS EC2 SLA
• “Unavailable” and...
Problem snapshot
Examples of preconditions
• For any SLA to apply, a number of preconditions typically exist per
provider
...
Problem snapshot
SLALOM Technical objectives
• To have a standard model for defining SLAs that eliminates ambiguities
• To...
Interaction with Standards
8
SLALOM@ISO
Interaction with ISO
• Mapped SLALOM 3-layer initial approach to ISO baseline model
– ISO approach powerful at ...
SLALOM@ISO
ISO 19086-2 Metric model
• SLALOM two-fold contribution:
– ISO model classes parameters: machine understandable...
SLALOM@ISO
SLALOM vs. ISO compliance
ISO-compliant SLA
• Usage of the ISO fields
(classes, parameters)
• SLA not necessari...
Mapping of Commercial SLAs
12
Commercial SLAs @SLALOM
Amazon WS EC2
Amazon EC2
Level / definition Expression Notes
Sample definition
sc: UNDEFINED (assu...
Commercial SLAs @SLALOM
Google AE Datastore
Google AppEngine Datastore
Level / definition Expression Notes
Sample definiti...
Commercial SLAs @SLALOM
Microsoft Azure
15
Microsoft Azure Storage
Level / definition Expression Notes
Sample definition
s...
SLA Comparability
16
SLA comparability
Overview
• Despite the fact that through the SLALOM / ISO model SLA descriptions
may be aligned, this do...
SLA comparability
Comparative metrics
• SLA success ratio
– Based on experience of usage of a service or provider
– In the...
SLA-related Lessons Learnt for Cloud Uptake
19
Lessons Learnt
Do
1) Target metrics that are directly comparable among providers
2) Consider directly machine understandab...
Lessons Learnt
Don’t
1) Consider that offered terms are equivalent, even if they originally seem to refer
to the same SLO....
SLALOM Contribution and Expected Impact
22
SLALOM contribution
Tender Evaluation
• Usable by various actors
– Adopters to specify their needs
– Providers to describe...
SLALOM contribution
Contract monitoring
• Benefits
– Achieve SLA non-repudiation
– Establish trust and transparency for se...
• SLALOM proposed specification / reference model already
takes into account:
– Standardisation approaches and working gro...
Contact us
26
• SLALOM Technicl WP Leader
ekaranas@mail.ntua.gr
vandro@mail.ntua.gr
gkousiou@mail.ntua.gr
• SLALOM Project...
SLALOM Project 27
SLALOM is a CSA financed by European
Commission under Grant agreement 644270
For more information on the...
Backup slides
SLA Strictness example
28
Backup sliSLA strictness example
29
Provider/Service t q (s1 * q) q’ (s2 * q) p (s3 * p) x S S’
Google Compute 0 5 (1.00) ...
Backup slides
Mapping of AWS EC2 SLA
30
AWS EC2 SLA @SLALOM (1/9)
Amazon EC2
Level / definition Expression Notes
Sample definition
sc: UNDEFINED (assumed ‘ping’->...
AWS EC2 SLA @SLALOM (2/9)
32
Abstract metric
definition
availability < 99.95 %
Availability metric definition given
the bo...
AWS EC2 SLA @SLALOM (3/9)
33
• Examples of preconditions:
– Deployment: Number of Availability Zones used
– Deployment: Re...
AWS EC2 SLA @SLALOM (4/9)
34
SAMPLE_001
Sample
definition
sc: UNDEFINED
(assumed ‘ping’-
> ICMP)
The sampling condition is...
AWS EC2 SLA @SLALOM (5/9)
35
Boundary period
and error
definitions
bp > 60 sec
The exact wording is “the percentage of
min...
AWS EC2 SLA @SLALOM (6/9)
36
PARAM_001
PARAM_002
SAMPLE_001
QDT_001
PARAM_001
PARAM_002SAMPLE_001
QDT_001
• Calculation of...
AWS EC2 SLA @SLALOM (7/9)
37
PARAM_001
PARAM_002
SAMPLE_001
QDT_001
• Calculation of Cloud Service Unavailability
• Based ...
AWS EC2 SLA @SLALOM (8/9)
38
PARAM_001
PARAM_002
SAMPLE_001
QDT_001
• Calculation of Cloud Service Availability
• Based on...
AWS EC2 SLA @SLALOM (9/9)
39
PARAM_001
PARAM_002
SAMPLE_001
QDT_001
• SLA Violation Condition
- i.e.: Availability < 99.95...
Backup slides
Mapping of GAE Datastore SLA
40
GAE Datastore SLA @SLALOM(1/11)
Google AppEngine Datastore
Level / definition Expression Notes
Sample definition
sc: INTER...
GAE Datastore SLA @SLALOM(2/11)
42
SAMPLE_001SAMPLE_001
PARAM_003
PARAM_002
PARAM_001
ER_001
DUR_001
QDT_001
UAP_001
BP_00...
GAE Datastore SLA @SLALOM(3/11)
43
• Examples of preconditions:
– Deployment: Number of Availability Zones used
– Deployme...
GAE Datastore SLA @SLALOM(4/11)
44
Sample
definition
sc: INTERNAL_ERROR
Several sampling conditions are
defined per type o...
GAE Datastore SLA @SLALOM(5/11)
45
Sample
definition
sc: INTERNAL_ERROR
Several sampling conditions are
defined per type o...
GAE Datastore SLA @SLALOM(6/11)
46
SAMPLE_001SAMPLE_001PARAM_003
PARAM_003
Boundary period
and error
definitions
bp > 300 ...
GAE Datastore SLA @SLALOM(7/11)
47
SAMPLE_001SAMPLE_001
PARAM_003
PARAM_002
PARAM_001
• Calculation of duration of samplin...
GAE Datastore SLA @SLALOM(8/11)
48
SAMPLE_001SAMPLE_001
PARAM_003
PARAM_002
PARAM_001
• Calculation of Unavailability Inte...
GAE Datastore SLA @SLALOM(9/11)
49
SAMPLE_001SAMPLE_001
PARAM_003
PARAM_002
PARAM_001
• Calculation of Unavailability peri...
GAE Datastore SLA @SLALOM(10/11)
50
SAMPLE_001SAMPLE_001
PARAM_003
PARAM_002
PARAM_001
ER_001
DUR_001
QDT_001
UAP_001
UAP_...
GAE Datastore SLA @SLALOM(11/11)
51
SAMPLE_001SAMPLE_001
PARAM_003
PARAM_002
PARAM_001
ER_001
DUR_001
QDT_001
UAP_001
BP_0...
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SLALOM Webinar Final Technical Outcomes Explanined "Using the SLALOM Technical Model to Improve #Cloud #SLA" v1

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SLALOM organized two live sessions to present the final versions of our legal terms and technical specifications for #Cloud #SLAs. The sessions provide examples showing how to practically apply SLALOM to improve current practice in the industry for # Cloud #SLAs and support development of cloud computing metrics.
The first webinar covered SLALOM Technical track "Using metrics to improve Cloud SLAs".

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SLALOM Webinar Final Technical Outcomes Explanined "Using the SLALOM Technical Model to Improve #Cloud #SLA" v1

  1. 1. Using the SLALOM model to improve Cloud SLAs Efstathios Karanastasis ICCS/NTUA
  2. 2. SLALOM Technical Track 2
  3. 3. Problem snapshot SLA Technological Landscape • A lot of ambiguities exist in SLAs of Cloud providers • The measurement/auditing process of an SLA cannot be done non-repudiably – i.e., the involved parties may be able to challenge the auditing of the SLOs • Standard models are rare and are not widely used • Differences between Cloud providers cannot be easily assessed – Absolute percentages cannot be compared among providers 3
  4. 4. Problem snapshot Ambiguities in SLAs • Availability (as defined by providers) definition may encapsulate different formulas for its calculation • The definition and calculation of availability may include different ways of identifying a failure, e.g.: – Response time less than a limit – Returned response within a string enumeration (i.e. a predefined range of string values) • Preconditions apply 4
  5. 5. Problem snapshot Real world example of Ambiguity • Ambiguity in the measurement process of AWS EC2 SLA • “Unavailable” and “Unavailability” mean: – When all of your running instances have no external connectivity • Determination of external connectivity. How? – Internet Layer: Pinging (ICMP)? • Security threat – Application layer: Endpoint checking? • Includes application downtime • Not exclusively the responsibility of AWS EC2 5
  6. 6. Problem snapshot Examples of preconditions • For any SLA to apply, a number of preconditions typically exist per provider • Examples: – Deployment: A specified number of Availability Zones must be used – Deployment: Replication options must be used – Usage/Measurement: Unavailable resources must first be restarted – Usage/Measurement: The number of request must be throttled 6
  7. 7. Problem snapshot SLALOM Technical objectives • To have a standard model for defining SLAs that eliminates ambiguities • To facilitate the measurement, monitoring and enforcement of SLAs to achieve non-repudiability • To abstract the SLA definition process (SLA  SLO  metric  sub- metric) so as to enable the application of metrics that allow for direct comparability 7
  8. 8. Interaction with Standards 8
  9. 9. SLALOM@ISO Interaction with ISO • Mapped SLALOM 3-layer initial approach to ISO baseline model – ISO approach powerful at describing more complex metrics (e.g. MS Azure SLA) • Demonstrated and suggested the ISO model Extendibility for fully defining the way an SLO can be audited – ACCEPTED – Suggested the inclusion of an Extension class in the ISO model – Instantiate the ISO Extension class as the base Sample class of SLALOM – Introduce the SLALOM Sample layer for concretely defining the sampling process – In the latest revision of the draft ISO model all classes are extendable • Applied on different types of Objectives of Commercial SLAs – GAE Datastore (PaaS) – AWS EC2 (IaaS) – Microsoft Azure (Storage) • Showed applicability of the proposed approach for directly creating machine understandable descriptions of the SLOs 9
  10. 10. SLALOM@ISO ISO 19086-2 Metric model • SLALOM two-fold contribution: – ISO model classes parameters: machine understandable – ISO model extension: definition of sampling process 10 SLALOM - proposed extension Model from the latest revision of the 19086-2 draft standard, to be made available in the forthcoming weeks All classes extendible
  11. 11. SLALOM@ISO SLALOM vs. ISO compliance ISO-compliant SLA • Usage of the ISO fields (classes, parameters) • SLA not necessarily fully defined 11 SLALOM-compliant SLA • ISO compliant • Clear and Well-defined • Non-repudiable • SLAs still not comparable among providers
  12. 12. Mapping of Commercial SLAs 12
  13. 13. Commercial SLAs @SLALOM Amazon WS EC2 Amazon EC2 Level / definition Expression Notes Sample definition sc: UNDEFINED (assumed ‘ping’-> ICMP) The sampling condition is not defined in the Amazon EC2 SLA. The concrete wording is “when all of your running instances have no external connectivity”. Nonetheless, the way to specify / measure “external connectivity” is not defined. For example, a customer could use a ping operation or a custom monitoring mechanism. Type of operation: ping Not defined how the condition of connectivity can be actually measured (e.g. the ping operation mentioned previously). Boundary period and error definitions bp > 60 sec The exact wording is “the percentage of minutes”, thus the period is 60 seconds. ec = 100% Error condition reflecting that the error ratio is that for the entire bp the resource must be continuously “unavailable”. Abstract metric definition availability < 99.95 % Availability metric definition given the boundary period and error condition. 13
  14. 14. Commercial SLAs @SLALOM Google AE Datastore Google AppEngine Datastore Level / definition Expression Notes Sample definition sc: INTERNAL_ERROR Several sampling conditions are defined per type of operation. For example it is specified (exact wording) “INTERNAL_ERROR, TIMEOUT, …” for API calls. Type of operation: API calls Several type of operations are defined. An example is provided here. Boundary period and error definitions bp > 300 sec The exact wording is “five consecutive minutes”. ec > 10% Error condition reflecting that the error ratio is (exact wording) “ten percent Error Rate”. Abstract metric definition availability < 99.95 % Availability metric definition given the boundary period and error condition. 14
  15. 15. Commercial SLAs @SLALOM Microsoft Azure 15 Microsoft Azure Storage Level / definition Expression Notes Sample definition sc = 60 sec Several sampling conditions are defined per type of operation. For example it is specified (exact wording) “Sixty (60) seconds” for PutBlockList and GetBlockList. Type of operation: PutBlockList and GetBlockList Several type of operations are defined. An example is provided here. Boundary period and error definitions bp > 3600 sec The exact wording is “given one-hour interval”. ec > 0% Error condition reflecting that all periods should be taken into account for the availability metric evaluation (exact wording) “is the sum of Error Rates for each hour”. Abstract metric definition availability < 99.9 % Availability metric definition given the boundary period and error condition.
  16. 16. SLA Comparability 16
  17. 17. SLA comparability Overview • Despite the fact that through the SLALOM / ISO model SLA descriptions may be aligned, this does not mean that SLAs (or their parameters) will be directly comparable • Need for more abstract metrics, that result in direct comparisons – SLA success ratio (Published* by Cloud WG of SPEC**) – SLA strictness (Published* by Cloud WG of SPEC+) – Standardised datasets • SLALOM model enables the application of comparable metrics – All SLA parameters are clearly and well defined – The SLAs are machine readable – Greatly simplifies the process and its automation * Ready for Rain? A View from SPEC Research on the Future of Cloud Metrics ** SPEC: Standard Performance Evaluation Corporation 17
  18. 18. SLA comparability Comparative metrics • SLA success ratio – Based on experience of usage of a service or provider – In the course of time keep track of successful or violated SLAs and total SLAs – Calculate the ratio: (Successful SLAs / Total SLAs) • SLA strictness – Extract static SLA parameters of importance for a given domain or application – Assign weights to parameters and normalise – Map these parameters to an arbitrary function – Results in a comparative ranking of different SLAs • Standardised datasets – Define a set of failure scenarios – Benchmark each provider SLA definition against the predefined scenario 18
  19. 19. SLA-related Lessons Learnt for Cloud Uptake 19
  20. 20. Lessons Learnt Do 1) Target metrics that are directly comparable among providers 2) Consider directly machine understandable descriptions via standardised templates 3) Look into the ISO 19086 series of standards and adopt if applicable 4) Think outside the narrow Cloud box. With the advent of *aaS and the emergence of IoT, SLAs may refer to services external to the data center or to specific metrics needed by Cloud Services based on the individual Use Case 5) Consider composite services that may create chains of SLAs and their interdependencies. For guaranteeing response time to service-support services consider downstream (reseller) and upstream (e.g. provider’s subcontractors) actors’ requirements and the need to ‘float’ SLA clauses down the chain 6) Consider resource management as a key part of SLA upkeep and analysis process 7) Consider mechanisms that would allow providers, resellers and users to easily monitor the SLA in a common and understandable way, even if not experts. 20
  21. 21. Lessons Learnt Don’t 1) Consider that offered terms are equivalent, even if they originally seem to refer to the same SLO. Always check the fine print for differences in how metrics are actually calculated 2) Consider that SLAs are monitored by providers. 3) Leave end users out of the loop. Comprehensiveness and clarity of an SLA (or its relevant metric) for non-experts should be a key target. Translate your metrics into plain English if necessary. 4) Limit yourself to popular metrics (e.g. availability) in SLAs. Users are also interested in more generic Quality of Experience (QoE) indexes such as stability 5) Expect the market to bend for you: fit in to current practice to the maximum extent and if not possible, hone your value proposition 21
  22. 22. SLALOM Contribution and Expected Impact 22
  23. 23. SLALOM contribution Tender Evaluation • Usable by various actors – Adopters to specify their needs – Providers to describe their value proposition – Third parties (resellers/brokers) to combine and offer services and suggest options • Added value – Application of comparative metrics – Automation of the process • Benefits – Improve transparency – Enhance efficiency – Establish fairness 23
  24. 24. SLALOM contribution Contract monitoring • Benefits – Achieve SLA non-repudiation – Establish trust and transparency for service execution compliant to the terms and proper violation management – Enable automation of contract and performance management and monitoring – Aid the involvement of actors like trusted third parties offering relevant services 24
  25. 25. • SLALOM proposed specification / reference model already takes into account: – Standardisation approaches and working groups outcomes – Current SLAs and metrics offered by commercial Cloud providers – Views expressed by Cloud providers and adopters – Research outcomes • Further feedback regarding applicability and practical usage of our model is more than welcome  • Please take the survey on IoT/Cloud metrics here: https://docs.google.com/forms/d/1JmwDXyO_1hT9iR-lm1c3LCQu_zF64nf-uFnxBeGMv3g/viewform 25 SLALOM contribution Your feedback needed
  26. 26. Contact us 26 • SLALOM Technicl WP Leader ekaranas@mail.ntua.gr vandro@mail.ntua.gr gkousiou@mail.ntua.gr • SLALOM Project Coordinator daniel.field@atos.net ?
  27. 27. SLALOM Project 27 SLALOM is a CSA financed by European Commission under Grant agreement 644270 For more information on the initiative contact us: @CloudSLAlom www.SLALOM-Project.eu SLALOM Project Coordinator (daniel.field@atos.net)
  28. 28. Backup slides SLA Strictness example 28
  29. 29. Backup sliSLA strictness example 29 Provider/Service t q (s1 * q) q’ (s2 * q) p (s3 * p) x S S’ Google Compute 0 5 (1.00) 5 (0.10) 99.95 (0.50) 0 0.50 1.60 Amazon EC2 0 1 (0.20) 1 (0.02) 99.95 (0.50) 0 1.30 1.48 MS Azure Compute 1 1 (0.20) 1 (0.02) 99.95 (0.50) 0 2.30 2.48 • Extract static SLA parameters of importance for a given domain/application – All these parameters (e.g. boundary period, error rates) are described in the SLALOM model • Map these parameters to an arbitrary Function, e.g.: , where: – q: size of the boundary period – p: percentage of availability – t: running time vs. overall monthly time (boolean), t ϵ {0,1} – x: existence of performance metrics (boolean), x ϵ {0,1} – si: normalisation factor for the continuous variables so that: (s1*q) ϵ [0,1], (s2*q) ϵ [0,0.1] and (s3*p) ϵ [0,0.5] • Resulting value may be compared between providers S = t + (1 - s1/2q) + s3p + x
  30. 30. Backup slides Mapping of AWS EC2 SLA 30
  31. 31. AWS EC2 SLA @SLALOM (1/9) Amazon EC2 Level / definition Expression Notes Sample definition sc: UNDEFINED (assumed ‘ping’-> ICMP) The sampling condition is not defined in the Amazon EC2 SLA. The concrete wording is “when all of your running instances have no external connectivity”. Nonetheless, the way to specify / measure “external connectivity” is not defined. For example, a customer could use a ping operation or a custom monitoring mechanism. Type of operation: ping Not defined how the condition of connectivity can be actually measured (e.g. the ping operation mentioned previously). Boundary period and error definitions bp > 60 sec The exact wording is “the percentage of minutes”, thus the period is 60 seconds. ec = 100% Error condition reflecting that the error ratio is that for the entire bp the resource must be continuously “unavailable”. Abstract metric definition availability < 99.95 % Availability metric definition given the boundary period and error condition. 31
  32. 32. AWS EC2 SLA @SLALOM (2/9) 32 Abstract metric definition availability < 99.95 % Availability metric definition given the boundary period and error condition. Condition of SLA violation specification Availability threshold specification Availability definition and calculation Billing period specification Unavailability definition and calculation Unavailability interval definition and calculation Boundary period specification Unreachable sample specification Sample definition and retrieval PARAM_001 PARAM_002 SAMPLE_001 QDT_001 UAP_001 BP_001 CFA_002 PARAM_003 CONDITION
  33. 33. AWS EC2 SLA @SLALOM (3/9) 33 • Examples of preconditions: – Deployment: Number of Availability Zones used – Deployment: Replication options used – Usage/Measurement: Restarting of resources when unavailable – Usage/Measurement: Applied Throttling of requests • Practical suggestions: – The strict definition of the Rules class to be concerning the necessary preconditions to apply – Note field as placeholder for the actual SLA text that refers to a given block
  34. 34. AWS EC2 SLA @SLALOM (4/9) 34 SAMPLE_001 Sample definition sc: UNDEFINED (assumed ‘ping’- > ICMP) The sampling condition is not defined in the Amazon EC2 SLA. The concrete wording is “when all of your running instances have no external connectivity”. Nonetheless, the way to specify / measure “external connectivity” is not defined. For example, a customer could use a ping operation or a custom monitoring mechanism. Type of operation: ping Not defined how the condition of connectivity can be actually measured (e.g. the ping operation mentioned previously). SAMPLE_001
  35. 35. AWS EC2 SLA @SLALOM (5/9) 35 Boundary period and error definitions bp > 60 sec The exact wording is “the percentage of minutes”, thus the period is 60 seconds. ec = 100% Error condition reflecting that the error ratio is that for the entire bp the resource must be continuously “unavailable”. PARAM_001 PARAM_002 SAMPLE_001 PARAM_001 PARAM_002
  36. 36. AWS EC2 SLA @SLALOM (6/9) 36 PARAM_001 PARAM_002 SAMPLE_001 QDT_001 PARAM_001 PARAM_002SAMPLE_001 QDT_001 • Calculation of Cloud Service Unavailability Interval • Based on: - The current sample - The defined boundary period - The definition of unreachable sample QDT_001 SAMPLE_001 PARAM_001 PARAM_002
  37. 37. AWS EC2 SLA @SLALOM (7/9) 37 PARAM_001 PARAM_002 SAMPLE_001 QDT_001 • Calculation of Cloud Service Unavailability • Based on: - The Cloud Service Unavailability Interval QDT_001 QDT_001 UAP_001 UAP_001 UAP_001
  38. 38. AWS EC2 SLA @SLALOM (8/9) 38 PARAM_001 PARAM_002 SAMPLE_001 QDT_001 • Calculation of Cloud Service Availability • Based on: - Billing period - The Cloud Service Unavailability UAP_001 UAP_001 UAP_001 UAP_001 BP_001 BP_001 BP_001 BP_001 BP_001 CFA_002 CFA_002 CFA_002
  39. 39. AWS EC2 SLA @SLALOM (9/9) 39 PARAM_001 PARAM_002 SAMPLE_001 QDT_001 • SLA Violation Condition - i.e.: Availability < 99.95% UAP_001 BP_001 CFA_002 CFA_002 CFA_002 PARAM_003 PARAM_003 PARAM_003 PARAM_003 ASV_001 ASV_001 ASV_001
  40. 40. Backup slides Mapping of GAE Datastore SLA 40
  41. 41. GAE Datastore SLA @SLALOM(1/11) Google AppEngine Datastore Level / definition Expression Notes Sample definition sc: INTERNAL_ERROR Several sampling conditions are defined per type of operation. For example it is specified (exact wording) “INTERNAL_ERROR, TIMEOUT, …” for API calls. Type of operation: API calls Several type of operations are defined. An example is provided here. Boundary period and error definitions bp > 300 sec The exact wording is “five consecutive minutes”. ec > 10% Error condition reflecting that the error ratio is (exact wording) “ten percent Error Rate”. Abstract metric definition availability < 99.95 % Availability metric definition given the boundary period and error condition. 41
  42. 42. GAE Datastore SLA @SLALOM(2/11) 42 SAMPLE_001SAMPLE_001 PARAM_003 PARAM_002 PARAM_001 ER_001 DUR_001 QDT_001 UAP_001 BP_001 CFA_002 PARAM_004 ASV_001 Condition of SLA Violation specification Availability threshold specification Availability definition and calculation Billing Period specification Unavailability definition and calculation Unavailability Interval definition and calculation Sampling Period duration definition and calculation Error Rate definition and calculation Boundary Period specification Error Rate threshold specification Unreachable sample values specification Sample definition and retrieval Abstract metric definition availability < 99.95 % Availability metric definition given the boundary period and error condition.
  43. 43. GAE Datastore SLA @SLALOM(3/11) 43 • Examples of preconditions: – Deployment: Number of Availability Zones used – Deployment: Replication options used – Usage/Measurement: Restarting of resources when unavailable – Usage/Measurement: Applied Throttling of requests • Practical suggestions: – The strict definition of the Rules class to be concerning the necessary preconditions to apply – Note field as placeholder for the actual SLA text that refers to a given block
  44. 44. GAE Datastore SLA @SLALOM(4/11) 44 Sample definition sc: INTERNAL_ERROR Several sampling conditions are defined per type of operation. For example it is specified (exact wording) “INTERNAL_ERROR, TIMEOUT, …” for API calls. Type of operation: API calls Several type of operations are defined. An example is provided here. SAMPLE_001SAMPLE_001 SAMPLE_001
  45. 45. GAE Datastore SLA @SLALOM(5/11) 45 Sample definition sc: INTERNAL_ERROR Several sampling conditions are defined per type of operation. For example it is specified (exact wording) “INTERNAL_ERROR, TIMEOUT, …” for API calls. Type of operation: API calls Several type of operations are defined. An example is provided here. SAMPLE_001SAMPLE_001PARAM_003 PARAM_003
  46. 46. GAE Datastore SLA @SLALOM(6/11) 46 SAMPLE_001SAMPLE_001PARAM_003 PARAM_003 Boundary period and error definitions bp > 300 sec The exact wording is “five consecutive minutes”. ec > 10% Error condition reflecting that the error ratio is (exact wording) “ten percent Error Rate”. PARAM_002 PARAM_002 PARAM_001 PARAM_001
  47. 47. GAE Datastore SLA @SLALOM(7/11) 47 SAMPLE_001SAMPLE_001 PARAM_003 PARAM_002 PARAM_001 • Calculation of duration of sampling period: - The period during which a number of samples was received - Period duration calculation based on samples timestamp • Calculation of actual Error Rate for sampling period: - Number of violation samples / number of total samples - Violation samples: samples containing values from a specific values pool ER_001 ER_001 SAMPLE_001 SAMPLE_001 PARAM_003 SAMPLE_001DUR_001 DUR_001 SAMPLE_001 DUR_001 ER_001 PARAM_003
  48. 48. GAE Datastore SLA @SLALOM(8/11) 48 SAMPLE_001SAMPLE_001 PARAM_003 PARAM_002 PARAM_001 • Calculation of Unavailability Interval - IF [Sampling Period duration > Boundary Period] - AND IF [Error Rate > Thershold (10%)] - THEN [Unavailability Interval = Sampling Period duration] ER_001 ER_001 QDT_001 DUR_001 DUR_001 PARAM_001 PARAM_002 DUR_001 QDT_001 QDT_001 QDT_001 QDT_001 ER_001 PARAM_002 DUR_001 PARAM_001 QDT_001 DUR_001
  49. 49. GAE Datastore SLA @SLALOM(9/11) 49 SAMPLE_001SAMPLE_001 PARAM_003 PARAM_002 PARAM_001 • Calculation of Unavailability period - It equals the SUM of Unavailability Intervals ER_001 DUR_001 QDT_001 QDT_001 UAP_001 UAP_001 UAP_001 QDT_001 UAP_001
  50. 50. GAE Datastore SLA @SLALOM(10/11) 50 SAMPLE_001SAMPLE_001 PARAM_003 PARAM_002 PARAM_001 ER_001 DUR_001 QDT_001 UAP_001 UAP_001 BP_001 BP_001BP_001 BP_001 CFA_002 CFA_002 CFA_002 • Calculation of Cloud Service Availability • Based on: - Billing period - The Cloud Service Unavailability CFA_002 BP_001 UAP_001
  51. 51. GAE Datastore SLA @SLALOM(11/11) 51 SAMPLE_001SAMPLE_001 PARAM_003 PARAM_002 PARAM_001 ER_001 DUR_001 QDT_001 UAP_001 BP_001 CFA_002 • SLA Violation Condition - i.e.: Availability < 99.95% PARAM_004CFA_002 CFA_002 PARAM_004 PARAM_004 PARAM_004 ASV_001 ASV_001 ASV_001

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