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USRE37811E1 - Distributed application load distribution aid tool - Google Patents

Distributed application load distribution aid tool Download PDF

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Publication number
USRE37811E1
USRE37811E1 US09/614,223 US61422300A USRE37811E US RE37811 E1 USRE37811 E1 US RE37811E1 US 61422300 A US61422300 A US 61422300A US RE37811 E USRE37811 E US RE37811E
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Prior art keywords
master
load
machine
machines
load data
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US09/614,223
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English (en)
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Gerard Sitbon
Francois Urbain
Therese Saliba
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Bull SAS
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Bull SAS
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5083—Techniques for rebalancing the load in a distributed system

Definitions

  • the present invention relates to a toolkit for balancing the load of an application distributed among several machines belonging to a distributed data processing system in a local area network.
  • the present invention constitutes an improvement and a generalization of the preceding solution.
  • the tool at the service of a distributed application running on the machines of a distributed data processing system in a local area network which is intended for balancing the load on each of these machines, is characterized in that it comprises a plurality of data processing modules called DAEMONs which run on these machines, one of which is called the master, the others being agents,
  • the master and the agents each having means for calculating the load of the machines on which they are running, at first predetermined sampling instants, and means for storing the load data of the master and the agents,
  • the local agent closest to the application indicating to the latter, at the request of the latter, the machine with the lightest load, the application then making the decision to request this machine to execute the services it needs.
  • FIG. 1 shows a distributed data processing system which includes the load balancing toolkit according to the invention.
  • FIG. 2 shows the controller for distributing the master-agent roles among the various elements which constitute the load balancing toolkit according to the invention.
  • load balancing toolkit ORC for (load balancing toolkit, in English) (load balancing toolkit, in English) in a distributed data processing system according to the invention—for simplicity's sake, from this point forward, it will be called the “toolkit”—appear in FIG. 1 .
  • the distributed data processing system which can be any type whatsoever, here called SID, comprises four data processing machines of any size and shape whatsoever, namely MCO, MC 1 , MC 2 , MC 3 .
  • MCO multi-processor
  • MC 1 a multi-processor
  • MC 2 a multi-processor
  • MC 3 a multi-processor system
  • CPU central processor unit
  • I/O units input/output units
  • the toolkit ORC itself comprises the master MS 0 and the three agents A 1 , A 2 , A 3 . Any agent can also be a master, depending on conditions which will be explained below. Both the master and the agents are constituted by data processing tools known to one skilled in the art as DAEMONs.
  • DAEMON is a data processing tool or entity running on a machine, which is capable of responding to a question.
  • the DAEMONs MS 0 , A 1 , A 2 , A 3 are respectively associated with shared memories MPO, MP 1 , MP 2 , MP 3 . Each of them contains the load of the corresponding machine, but also the loads of the other machines in SID.
  • FIG. 1 assumes that the distributed application is running on the machine MC 2 and that it requires services supplied by the other machines MCO, MC 1 , MC 3 .
  • This application is designated APU.
  • the places where the master and the agents are located are independent from the place where APU is running.
  • Each agent as well as the master collects, for the machine on which it is running, at given time intervals which constituted the first determined sampling instants ti, the load data of this machine, for each of the elements which constitute it (the CPU load, the loads of the associated memories, the loads of the I/Os, the network load, etc.). From the load of each element, expressed as a percentage of its maximum allowable load, the total load of the machine in question is calculated. This is accomplished by load calculation means, respectively MCC 0 for MS 0 , MCC 1 for A 1 , MCC 2 for A 2 , MCC 3 for A 3 .
  • OPERATION 2 At regular intervals, the agents send MS 0 the statistical load data from the corresponding machine, through the network (for A 2 , through the link L 2 between MC 2 and MC 0 ).
  • OPERATION 3 The master MS 0 centralizes, at practically the same regular intervals defined for Operation 2 , which constitute the second determined sampling instants Ti, all the statistical load data of all the agents, as well as its own, at the level of its associated shared memory, in this case MP 0 .
  • This centralization is in fact an operation for collecting the load data. It is therefore executed by load data collection means, respectively MRC 0 for MS 0 , MRC 1 for A 1 , MRC 2 for A 2 , MRC 3 for A 3 , which are in fact collection programs integrated into the master and into each of the agents A 1 through A 3 and are therefore not represented in FIG. 1 for simplicity's sake.
  • OPERATION 4 The master MS 0 sends, using sending means MTCO, all this data to each agent A 1 , A 2 , A 3 through the network RE, namely through the links L 1 between MC 0 and MC 1 , L 2 between MC 0 and MC 2 , L 3 between MC 0 and MC 3 , MTC 0 is an integral part of MS 0 and is therefore not represented in FIG. 1 for simplicity's sake.
  • Each agent receives this load data and copies it into its associated shared memory.
  • OPERATION 6 The application APU scans the shared memory of the machine on which it is running to search it for the load estimated for each of the machines, and at the moment it needs the determined services to be rendered, it deduces the machine with the lightest load at this moment and requests the latter to render it these services.
  • the total load Wt of an agent (and also of the master) is obtained using the following formula:
  • Wt k1*W1+k2*W2+k3*W3+k4*W4, in which:
  • W 1 is the percentage of the utilization of the central processor of the agent in terms of time
  • W 2 is the percentage of the utilization of the memory of the agent, that is, the ratio between the storage capacity actually used and its total capacity
  • W 3 is the percentage of the utilization of the network by the agent, that is, the ratio between the number of pieces of information sent and received by the agent and the maximum allowable rate in the network
  • W 4 is the percentage of the utilization of the input/output units by the agent.
  • k 1 , k 2 , k 3 , k 4 are specific weighting factors of the processor, the memory, the network, and the input/output units. Their sum is equal to 1. Their values depend on the nature of the application in the process of running, in this case APU on the machine MC 2 .
  • the loads W 1 , W 2 , W 3 , W 4 are measured and Wt is calculated as shown in each of the tables in Appendix 1, at determined sampling instants t1, t2, t3, t4, t5, t6, t7, etc. of the period T (which are, in fact, the instants ti mentioned above in the description of Operation 1).
  • Table 1 gives an example of load data collected by any agent, for example A 1 , relative to the corresponding machine MC 1 , for all the instants t1 through t7. This data is, of course, stored in the shared memory MP 1 of the machine MC 1 on which A 1 is running, before it is sent to MS 0 .
  • This table shows, for example, that W 1 is equal to 35 at the instant t1, W 2 to 67 at the instant t4, W 3 to 38 at t6, W 4 to 32 at t7, and so on.
  • a calculation program API associated with APU, which runs on MC 2 then applies—for the load data of each agent and the master which, after the execution of Operation 3, is contained in the shared memory MP 0 of MC 0 associated with MS 0 —the weighting factors k 1 through k 4 specific to the corresponding machines for the application APU.
  • Table 2 shows, for each of the machines MCO through MC 4 , the global load value Wt at the instants t1 through t7.
  • Wt is equal to 56 at the instant t1, 32 at t2, 67 at t3, etc.
  • Wt is equal to 23 at t1, 34 at t2, etc., and so on for the other machines.
  • Table 3 is obtained. This table makes it possible to read, for example, that the estimated values of the loads of MCO through MC 3 at this instant t8 are 73, 82, 36, and 76, respectively.
  • a power coefficient Cp specific to each machine is applied to the total load of each machine in order to obtain its actual available capacity rate C 1 , using the formula:
  • the coefficient Cp of a given machine is calculated by generating a synthesis of the power of the central processor CPU, the capacity of the memories, the processing power of the input/output units, etc. It is recalculated each time the hardware configuration of the machine is changed or its operating system is modified. Likewise, each time the general configuration of the distributed data processing system SID is changed, all the coefficients CP of all the machines in the system are redefined.
  • a Cp equal to 1 corresponds to a medium-scale machine, which is defined by the user.
  • the selection mechanism must ensure that at least 1 DAEMON is running and that two of them cannot be masters simultaneously (especially if they start up at the same time).
  • Phase 1 When a DAEMON starts up, it generates a unique identifier ID in conformity with the protocol used in the network RE, for example in conformity with the TCP-IP protocol used in the exemplary embodiment of the invention described herein.
  • This identifier is composed of the Ethernet address (Ethernet is the part of the TCP-IP protocol related to local area networks and is used in the exemplary embodiment described herein. Ethernet being understood to be standardized and therefore known to one skilled in the art), the moment at which the identifier is transmitted, and a random value. At the same time, it puts itself into an intermediate state and sends these two pieces of information (its current state, ID) through the network RE to all the machines in the network.
  • Phase 2 it waits to receive the identical information from the other DEMONS, for determined a time interval Tr (on the order of 5 to 10 seconds). It is a candidate for the role of master.
  • Phase 3 As soon as it receives this information, it analyzes it.
  • Phase 4 Once this time interval has elapsed, the DAEMON in question tries again. In order to avoid a loss of messages, which is always possible in the network RE, it uses the following procedure:
  • the transmission and the listening for responses are repeated 5 times.
  • the DAEMON in question receives a response from another DAEMON which reveals itself to be an agent, it can be sure that a master MS 0 exists and it waits for the response from the latter to reach it.
  • Phase 5 When 5 repetitions have taken place, and the DAEMON in question has not received any response from the other DAEMONs, it then decides that it is the master MS 0 .
  • the master periodically notifies all the machines in the system SID of its existence. If the master detects the existence of another master, the procedure is restarted by the one whose ID is lower.
  • FIG. 2 which shows the controller AUT which distributes the master-agent roles among the various DAEMONs running on the machines of SID, will make it easier to understand the sequence of the different phases 1 through 5 described above.
  • This controller AUT comprises 5 states:
  • the DAEMON in question sends the two pieces of information (its own ID, its state), which corresponds to Phase 1 .
  • the DAEMON in question becomes an agent A 1 , A 2 or A 3 .
  • the DAEMON in question has disseminated its ID and its state and has set a time interval Tr.
  • e2 An ID is received, and the local identifier ID (that of the DAEMON in question) is lower than the identifier it receives.
  • e3 An ID is received, and the local ID is greater than or equal to the ID received.
  • e5 The time interval Tr has expired and the number of attempts is less than 5, or an agent has just responded.
  • e9 Detection by a master of the existence of another master with a higher ID.

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  • Software Systems (AREA)
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  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Multi Processors (AREA)
  • Computer And Data Communications (AREA)
US09/614,223 1994-11-30 1995-11-17 Distributed application load distribution aid tool Expired - Lifetime USRE37811E1 (en)

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FR9414386 1994-11-30
FR9414386A FR2727540B1 (fr) 1994-11-30 1994-11-30 Outil d'aide a la repartition de la charge d'une application repartie
PCT/FR1995/001514 WO1996017297A1 (fr) 1994-11-30 1995-11-17 Outil d'aide a la repartition de la charge d'une application repartie
US09/614,223 USRE37811E1 (en) 1994-11-30 1995-11-17 Distributed application load distribution aid tool
US08/836,483 US5993038A (en) 1994-11-30 1995-11-17 Distributed application load distribution aid tool

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DE (1) DE69521271D1 (fr)
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US20050160276A1 (en) * 2004-01-16 2005-07-21 Capital One Financial Corporation System and method for a directory secured user account
US20080034093A1 (en) * 2006-08-01 2008-02-07 Hiromi Sutou System and method for managing resources
US8930508B2 (en) * 2011-08-09 2015-01-06 Fujitsu Limited Device management apparatus, device management method, and computer-readable storage medium
US11968251B1 (en) * 2022-12-12 2024-04-23 Juniper Networks, Inc. Self-learning service scheduler for smart NICs
US12289240B2 (en) 2023-05-12 2025-04-29 Juniper Networks, Inc. Dynamic service rebalancing in network interface cards having processing units

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Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030153994A1 (en) * 2002-02-13 2003-08-14 Li-Jie Jin Method of load balancing a distributed workflow management system
US7127716B2 (en) * 2002-02-13 2006-10-24 Hewlett-Packard Development Company, L.P. Method of load balancing a distributed workflow management system
US20050160276A1 (en) * 2004-01-16 2005-07-21 Capital One Financial Corporation System and method for a directory secured user account
US20080034093A1 (en) * 2006-08-01 2008-02-07 Hiromi Sutou System and method for managing resources
US8046466B2 (en) * 2006-08-01 2011-10-25 Hitachi, Ltd. System and method for managing resources
US8930508B2 (en) * 2011-08-09 2015-01-06 Fujitsu Limited Device management apparatus, device management method, and computer-readable storage medium
US11968251B1 (en) * 2022-12-12 2024-04-23 Juniper Networks, Inc. Self-learning service scheduler for smart NICs
US12289364B2 (en) 2022-12-12 2025-04-29 Juniper Networks, Inc. Self-learning service scheduler for smart NICs
US12289240B2 (en) 2023-05-12 2025-04-29 Juniper Networks, Inc. Dynamic service rebalancing in network interface cards having processing units

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JPH10507024A (ja) 1998-07-07
WO1996017297A1 (fr) 1996-06-06
FR2727540B1 (fr) 1997-01-03
FR2727540A1 (fr) 1996-05-31
EP0715257B1 (fr) 2001-06-13
US5993038A (en) 1999-11-30
DE69521271D1 (de) 2001-07-19
EP0715257A1 (fr) 1996-06-05

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