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• Good resource allocation schemes are needed to fully utilize the computing capacity of the DS • Distributed scheduler is a resource management • It focuses on judiciously and transparently redistributing the load of the system among the computers • Target is to maximize the overall performance of the • A locally distributed system consists of a collection of autonomous computers connected by a local area communication network • Users submit tasks at their host computers for processing• Load distributed is required in such environment because of random arrival of tasks and their random CPU service time • There is a possibility that several computers are heavily loaded and others are idle of lightly loaded • If the load is heavier on some systems or if some processors execute tasks at a slower rate than others, this situation will occur often • Consider a system of N identical and independent • Identical means that all servers have the same task • Let ? be the utilization of each server, than P=1- ?, • If the ?=0.6, it means that P=0.4,• If the systems have different load than load can be transferred from highly loaded systems to lightly load systems to increase the performance – Resource queue lengths and particularly the CPU queue – Measuring the CPU queue length is fairly simple and – CPU queue length does not always tell the correct situation as the jobs may differ in types – Another load measuring criterion is the processor – Requires a background process that monitors CPU utilization continuously and imposes more overhead – Used in most of the load balancing algorithms • Basic function is to transfer load from heavily loaded systems to idle or lightly loaded systems • These algorithms can be classified as : • decisions are hard-wired in the algorithm using a prior knowledge • use system state information to make load distributing decisions • special case of dynamic algorithms in that they adapt their activities by dynamically changing the parameters of the algorithm to suit the changing system state – Load sharing algorithms strive to reduce the possibility for a system to go to a state in which it lies idle while at the same time tasks contend service at another, by transferring tasks to lightly loaded nodes – Load balancing algorithms try to equalize loads at al – Because a load balancing algorithm transfers tasks at higher rate than a load sharing algorithm, the higher overhead incurred by the load balancing algorithm may outweigh this potential performance improvement • Preemptive vs. Non-preemptive transfer – Preemptive task transfers involve the transfer of a task – Non-preemptive task transfers involve the transfer of the tasks that have not begun execution and hence do not require the transfer of the task’s state – Preemptive transfer is an expensive operation as the collection of a task’s state can be difficult – What does a task’s state consist of?– Non-preemptive task transfers are also referred to as task – determines whether a node is in a suitable state to participate in a – requires information on the local nodes’ state to make decisions – determines which task should be transferred – determines to which node a task selected for transfer should be – requires information on the states of remote nodes to make – responsible for triggering the collection of system state information– Three types are: Demand-Driven, Periodic, State-Change-Driven • A system is termed as unstable if the CPU queues grow without bound when the long term arrival rate of work to a system is greater than the rate at which the system can perform work.
• If an algorithm can perform fruitless actions indefinitely with finite probability, the algorithm is said to be unstable. • Activity is initiated by an overloaded node (sender)• A task is sent to an underloaded node (receiver) • A node is identified as a sender if a new task originating at the node makes the queue length exceed a threshold T.
• Only new arrived tasks are considered for transfer • Random: dynamic location policy, no prior information exchange• Threshold: polling a node (selected at random) to find a receiver• Shortest: a group of nodes are polled to determine their queue • Location policies adopted cause system instability at high loads Yes QueueLength at “I”

• Initiated from an underloaded node (receiver) to obtain a task from an overloaded node (sender) • A node selected at random is polled to determine if transferring a task from it would place its queue length below the threshold level, if not, the polled node transfers a task.
• Do not cause system instability in high system load, however, in • Most transfers are preemptive and therefore expensive Yes QueueLength at “I”
Task Departure at “j”
• Both senders and receivers search for receiver and senders, respectively, for task transfer.
• Thresholds are equidistant from the node’s estimate of the • Sender-initiated component: Timeout messages TooHigh, TooLow, Accept, AwaitingTask, ChangeAverage • Receiver-initiated component: Timeout messages TooLow, LooHigh, Accept, AwaitingTask, ChangeAverage • Similar to both the earlier algorithms • A demand-driven type but the acceptable range can be increased/decreased by each node individually.
• A Stable Symmetrically Initiated Algorithm – Utilizes the information gathered during polling to classify the nodes in the system as either Sender, Receiver or OK.
– The knowledge concerning the state of nodes is maintained by a data structure at each node, comprised of a senders list, a receivers list, and an OK list.
– Initially, each node assumes that every other node is a receiver.
– Transfer Policy • Triggers when a new task originates or when a task departs.
• Makes use of two threshold values, i.e. Lower (LT) and Upper (UT) • Sender-initiated component: Polls the node at the head of receiver’s list• Receiver-initiated component: Polling in three order – Head-Tail (senders list), Tail-Head (OK list), Tail-Head (receivers list) – Selection Policy: Newly arrived task (SI), other approached (RI)– Information Policy: A demand-driven type • Receiver-initiated task transfers can improve system performance at high system loads.
• Receiver-initiated transfers require – Task Placement refers to the transfer of a task that is yet to begin execution to a new location and start its execution there.
– Task Migration refers to that transfer of a task that has already begun execution to a new location and continuing its execution there.
• The transfer of the task’s state including information e.g. registers, stack, ready/blocked, virtual memory address space, file descriptors, buffered messages etc. to the new machine.
• The task is frozen at some point during the transfer so that the state does not change further.
• The task is installed at the new machine and is put in the ready queue so that it can continue executing.
– To support remote execution, obtaining and transferring the state, and Residual Dependencies
– Refers to the amount of resources a former host of a preempted or migrated task continues to dedicate to service requests from the migrated task.
• Attempts to reduce the freezing time of a migrating task by precopying the state.
• The bulk of the task state is copied to the new host• It increases the number of messages that are sent to new host • Makes use of the location-transparent file access mechanism provided by its file • All the modified pages of the migrating task are swapped to file server • Reduction in migration is achieved by using a feature called Copy-on-Reference• The entire virtual memory address space is not copied to the new host • Services that are provided to user processes irrespective of the location of the processes and services.
• In distributed systems, it is essential that the location • Location transparency in principle requires that names (e.g. process names, file names) be independent of their location (i.e. host names).
• Any operation (such as signaling) or communication that was possible before the migration of a task should be possible after its migration • Example – SPRITE – Location Transparency Mechanisms – A location-transparent distributed file system is provided– The entire state of the migrating task is made available at the new host, and therefore, any kernel calls made will be local at new host – Location-dependent information such as host of a task is maintained • Issues involved in Migration Mechanisms – Decision whether to separate the policy-making modules • It has implications for both performance and the ease of • The separation of policy and mechanism modules simplifies the – Decision to where the policy and mechanisms should • The migration mechanism may best fit inside the kernel• Policy modules decide whether a task transfer should occur, this – Interplay between the task migration mechanism and • The mechanisms can be designed to be independent of one another so that if one mechanism’s protocol changes, the other’sneed not • Comparing the performance of task migration mechanisms implemented in different systems is a difficult task, because of the different, • SPRITE consists of a collection of SPARCSTATION 1• CHARLOTTE consists of VAX/11-750 machines – Operating systems– IPC mechanism– File systems– Policy mechanisms



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