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Walter Binder University of Lugano, Switzerland Niranjan Suri IHMC, Florida, USA Green Computing: Energy Consumption Optimized Service Hosting.

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Presentation on theme: "Walter Binder University of Lugano, Switzerland Niranjan Suri IHMC, Florida, USA Green Computing: Energy Consumption Optimized Service Hosting."— Presentation transcript:

1 Walter Binder University of Lugano, Switzerland Niranjan Suri IHMC, Florida, USA Green Computing: Energy Consumption Optimized Service Hosting

2 Motivation Data centers are becoming ubiquitous Large installations of computer systems Providing critical services Data centers are big power consumers Continuously operating computers, regardless of the load Cooling

3 Reducing Power Consumption Green Grid consortium advocates data center design and management to improve energy efficiency Right-sizing data centers at design time Energy-efficient cooling Virtualization (multiple servers on same physical machine) Processor power saving (e.g., clock rate depending on load) Powering down unused machines Computers with dedicated roles (e.g., computers performing backups)

4 Our Approach Load on machines varies over time Turn off subset of unnecessary machines, respectively restart machines according to load Problems Load is distributed over multiple machines Load reduction typically also distributed across multiple machines Need to consolidate load on a subset of machines in order to free up machines that can be turned off Goal: Minimum number of machines running Constraint: QoS must be ensured Service-Level Agreements (SLAs) must not be violated

5 Example

6 Service Types Hosting environment may offer multiple service types Service type consists of Service interface SLA defining QoS parameters SLA parameters specified according to a common ontology WS-Agreement, WSLA, SLAng, etc. Here: Single QoS parameter: Response time

7 Stateless versus Stateful Services Stateless service: Requests are independent After completing all pending requests, a stateless service may be stopped Stateful service: Requests in one session may depend on prior requests in the same session Sessions may be explicitly terminated by clients, or expire after some period of inactivity After termination of all sessions, a stateful service may be stopped

8 Hosting Environment (1) Dedicated machines for three different purposes: File servers Provide all data sources Compute servers Execute service requests Dispatchers Receive service requests and choose compute servers to handle them Decide on shutdown and restart of compute servers Dispatchers and file servers are continuously running Only idle compute servers may be shut down

9 Hosting Environment (2) Compute servers File serversDispatcher Clients requests dispatch data access

10 Hosting Environment (3) Heterogeneous environment Machines have different computing resources Dynamically changing environment New machines may be added Cores may fail Compute servers may host any number of service types, and a service type may be hosted by any number of compute servers Compute servers are ranked according to energy efficiency

11 Node Manager Each compute server runs a Node Manager component Monitors idle time and average response time for each service type Communicates measurements to dispatcher Handles server shutdown upon request from dispatcher Notifies dispatcher upon startup

12 Shutdown of Compute Severs Dispatcher notifies Node Manager on compute server to prepare shutdown No further service requests are dispatched to the compute server Node Manager waits for Completion of all previously accepted requests Termination of all active sessions Alternative: Migration of sessions

13 Shutdown Options Complete shutdown No power consumption Ensures clean state upon restart (e.g., no memory leaks) Slow restart Hibernation No power consumption Memory saved on persistent storage Resume by reloading memory snapshot Standby Reduced power consumption Processor stopped, but memory remains active Fast restart

14 Restart of Compute Servers Wake on LAN Magic packet is broadcast to LAN Special header: 0xFF repeated 6 times MAC address of the machine to restart Dispatcher initiates compute server restart Node Manager notifies dispatcher of completed restart Dispatcher needs to know MAC addresses of all compute servers

15 Service Dispatch: Definitions n compute servers Sorted according to energy efficiency s x more energy efficient than s y  x < y In each configuration s 1 … s r are running (1 ≤ r ≤ n) s r … s n are shut down (or in the process of shutting down) p T (i): probability that request for service type T is dispatched to s i

16 Service Dispatch upon Request Take a random number z (0 ≤ z ≤ 1; uniform distribution) Choose s c such that c = min { i: (1 ≤ i ≤ n) && (z ≤ sum(1; i; p T (i))) } Related to lottery scheduling Tickets instead of probabilities

17 Update of Probabilities (1) In regular intervals, dispatcher obtains monitoring data from Node Managers of running compute servers If s i had idle time and s i had no problem meeting the SLAs: Increase load on s i, reduce load on s r p T (r) := p T (r) – Δ p p T (i) := p T (i) + Δp If r > 1 and for all service types T p T (r) = 0, initiate shutdown of s r

18 Update of Probabilities (2) If compute server s i violates the SLA for a service type T (overload situation): First try to find a running compute server s k (1 ≤ k ≤ r) that has idle time and met the SLAs of all service types Balance load between s i and s k p T (i) := p T (i) – Δ p p T (k) := p T (k) + Δp If there is no such compute server s k, initiate restart of s r+1

19 Future Work (1) Testbed and evaluation Main evaluation metric: Energy savings for given workloads Service performance must be modeled Traces of service execution in data centers needed Migration of sessions Reduces the time for preparing shutdown Complex optimization criteria Minimize number of service types hosted on the same compute server Consider estimated shutdown preparation time when choosing the compute server to shut down

20 Future Work (2) Distribution and replication Service dispatcher must not become bottleneck Fault tolerance Dispatcher must detect compute server failures Dispatcher must not become single point of failure Sudden load fluctuations Shutting down machines increases vulnerability wrt. denial-of-service attacks

21 Conclusions Data centers are growing and consume huge amounts of electrical energy Energy can be saved by powering down unused machines according to the current load Requires consolidation of services on a subset of the available machines Probabilistic approach to energy consumption-aware load-balancing


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