DAY 2
Agenda for Today � Introduce the workload characterization problem. � Discuss a simple example of characterizing the workload for an intranet. – What is workload characterization? – What workload we want to characterize? � Present a workload characterization methodology.
References • Thyagaraj Thanalapati, Sivarama P. Dandamudi: An Efficient Adaptive Scheduling Scheme for Distributed Multicomputers. IEEE Trans. Parallel Distrib. Syst. 12(7): 758-768 (2001) • A. Iosup and D.H.J. Epema, Grid Computing Workloads, IEEE Internet Computing 15(2): 19-26 (2011) • Feitelson DG. Workload modeling for computer systems perfMemory ormance evaluation. Cambridge University Press; 2015.
Introduction • Workloads appear in many contexts and therefore have many different types. • Different workloads have a significant effect on performance. • Different workloads have different features • Ensuring that systems meet desired performance goals without excessive (and expensive) over- provisioning is of great importance. • Therefor, reliable workload models are needed.
What is workload? • The workload of a system can be defined as the set of all inputs that the system receives from its environment during any given period of time. A variety of jobs with different characteristics are submitted to the cloud by different users. Need to capture A static workload is one in which a certain amount of work is given, and when it is done that is it. A dynamic workload, in contrast, is one in which work continues to arrive all the time; it is never “done”.
Sample Cloud Computing Applications High Tsunami Epidemic Social Web Prediction Simulation Gaming Exp. Server Space Survey Research Comet Detected Demand SW Social Big Data Variability Dev/Test Networking Analytics Online Pharma Gaming Research Taxes, Office Sky Survey @Home HP Engineering Tools Low High Low Demand Volume From Iosup
Batch workloads • Typically tend to process huge volumes of data. – Example: An application that produce monthly bills for utilities (e.g., power, water, mobile phone bills, etc.) – Require considerable compute and storage resources. – Batch workloads are rarely time sensitive and can be scheduled when few real-time tasks are running.
High-performance workloads • High-performance workloads typically require significant compute capabilities. – next day weather prediction application • High-throughput workloads Examples of these applications include – different patient data in large scale biomedical trials – different parts of a genome or protein sequence in bioinformatics applications – different random numbers in a simulations based on Monte Carlo methods – different model parameters in ensemble simulations or explorations of parameter spaces
Bag-of-Tasks • Bag-of-tasks refers to parallel jobs with many independent tasks • The tasks can be executed out of the submission order. • The following is not bag of task T3 T4 T6 T8 T1 T7 T5 T2
Example of BoT Applications • Parameter sweeps – Comprehensive, possibly exhaustive investigation of a model – Very useful in engineering and simulation-based science • Monte Carlo simulations – Simulation with random elements: fixed time yet limited inaccuracy – Very useful in engineering and simulation-based science • Many other types of batch processing – Periodic computation, Cycle scavenging – Very useful to automate operations and reduce waste
Workflow Applications • Workflow is a set of jobs with precedence (think Direct Acyclic Graph) • Complex applications – Complex filtering of data – Complex analysis of instrument measurements Maslina Abdul Aziz, Jemal H. Abawajy, Tutut Herawan: Layered workflow scheduling algorithm. FUZZ-IEEE 2015: 1-7
ADDRESSING BIG DATA ENERGY CONSUMPTION • Integrated Fog computing (FC) and Internet of Everything (IoE) Observation Station Collection Station Data Center Medical staff Application 1 Application N Alert Visualization Publish Analytics Engine Enigne Medical Application Services Middleware Push VM Service Data Sharing Policy Manager Scheduler Push Push Access Control Pull Pull Pull Virtual (Sensor) Machines Monitoring Centre Fog Node Computational E-Health Sensor resources Data Data IoT+Fog Emergency Patient Physical Machines Jemal H. Abawajy, Mohammad Mehedi Hassan, Federated Internet of Things and Cloud Computing Pervasive Patient Health Monitoring System. IEEE Communications Magazine 55(1): 48-53 (2017) Sara Ghanavati, Jemal Abawajy and Davood Izadi (2017), The Internet of Things: Foundation for Smart Cities, eHealth, and Ubiquitous Computing, Ricardo Armentano, Robin Singh Bhadoria, Parag Chatterjee, Ganesh Chandra Deka (eds.), CRC Press.
Workflow Applications • Workflow applications are jobs with precedence ( represented by a directed acyclic task graph). • 75%+ WFs are sized 40 jobs or less, 95% are sized 200 jobs or less ( Ostermann et al., 2008) T3 T4 T6 T8 T1 T7 T5 T2 Structure of divide-and-conquer programs • Example applications – Complex filtering of data – Complex analysis of instrument measurements Maslina Abdul Aziz, Jemal H. Abawajy, Tutut Herawan: Layered workflow scheduling algorithm. FUZZ-IEEE 2015: 1-7
Web Workload • Example of execution of HTTP requests (sec) � Characteristics of Web workloads: – burstiness Request No. CPU I/O Elapsed time time time – heavy-tailed distributions 1 0.0095 0.04 0.071 2 0.0130 0.11 0.145 3 0.0155 0.12 0.156 4 0.0088 0.04 0.065 5 0.0111 0.09 0.114 6 0.0171 0.14 0.163 7 0.2170 1.20 4.380 8 0.0129 0.12 0.151 9 0.0091 0.05 0.063 10 0.0170 0.14 0.189 Average 0.0331 0.205 0.550
Performance Analysis • There are three common methodologies to evaluate performance – Use actual workload – Use traced workload directly to drive a simulation. – Create a model from the trace and use the model for either analysis or simulation.
Trace Data • Trace is recorded data as a trace, or log, of workload-related events that happened in a certain system. • Example – The Failure Trace Archive (FTA) is a centralized public repository of availability traces of parallel and distributed systems, and tools for their analysis. – The purpose of this archive is to facilitate the design, validation, and comparison of fault- tolerant models and algorithms.
Trace-data simulation • Trace-driven simulations are common in parallel and distributed computing • Shortcomings – Collecting data may be inconvenient or costly, and instrumentation may introduce overhead. – Data collection is inherently limited as the conditions under which data is gathered may restrict one to a small sample of the space of interest. – For example, given a server with 128 nodes, it is not possible to collect data about systems with 64 or 256 nodes. • We need models to transcend these limitations.
Workload Modeling • Workload modeling is used to generate synthetic workloads based on real-life job execution observations. • The goal is typically to be able to create workloads that can be used in performance evaluation studies Negotiation Workload SLA Workload Submission Workload description (may Generation include QoS) Consolidate J1 Jn J1 result Analysis
Framework End Workload User Model The synthetic workload is supposed to be similar to those that occur in practice on real systems Real Synthetic Workload Workload Replicate in experimental setting cloud environment Cloud Computing Cloud Computing Environment Environment The result is supposed to align with real application Performance Performance Measures P real Measures P model
Workload Resource Usage Observation Transaction Frequency Maximum CPU time Maximum I/O time (msec) Classes (msec) Trivial 40% 8 120 Light 30% 20 300 Medium 20% 100 700 Heavy 10% 900 1200
Workload Features • Workloads are characterized by many attributes that have a great effect on scheduling – I/O can also have a great effect on scheduling – memory can lead to swapping. – for parallel jobs, the number of processors used is an additional parameter, which influences how well jobs pack together. • The level of detail needed in workload characterization depends on the goal of the evaluation. Jemal H. Abawajy, Sivarama P. Dandamudi: Scheduling Parallel Jobs with CPU and I/O Resource Requirements in Cluster Computing Systems. MASCOTS 2003: 336-343
Workload Features • Workloads are characterized by many attributes – arrival times – running times – total memory usage (maybe locality of reference) – distribution of I/O sizes and how they interleave with the computation – Communication – number of processors (VMs)
Selection of characterizing parameters • Each workload component is characterized by two groups of information: • Workload intensity – arrival rate – number of clients and think time – number of processes or threads in execution simultaneously • Service demands (D i1 , D i2 , … D iK ), where D ij is the service demand of component i at resource j. Adapted from Menascé & 52 Almeida.
Exponential Distribution • The Exponential: 𝑔 𝑦 = 𝜇 ∙ 𝑓 −𝜇∙𝑦 – 𝜇 = measures how many things happen per unit 2.5 probability density functions 2 Lambda 0.5 1 2 1.5 1 0.5 0 1 2 3 4 5 6 7 X
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