面向用户的云计算环境下敏捷软件模式优化资源调度方法
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本文主要探讨了在云计算环境下，为了提升敏捷软件模式而提出的一种用户中心的资源调度方法。随着云计算的发展，众包作为一种服务模式日益流行，它强调跨学科合作，与客户共创价值。在这种背景下，软件开发过程被视为一种软件生命周期中的活动集合，为软件工程的不同阶段提供了一套规则，以实现预定目标。
当前，由于软件开发周期不断缩短以及需求变化频繁，传统的软件开发策略面临挑战。为了适应这种快速变化的环境并提高敏捷性，研究人员Long Fei、杨昌、荣辉桂和李建方在《中南大学学报》（2016年）上发表的研究论文中，提出了一个以用户为中心的资源调度方案。该方法旨在优化云环境中软件开发流程，确保资源的有效分配和利用，以满足用户的需求变化，并提高团队协作效率。
论文的核心观点包括以下几个方面：
1. **用户导向**：强调在设计和执行资源调度时，充分考虑用户的实际需求和满意度，确保开发过程更加贴近用户期望。
2. **敏捷性提升**：通过灵活的资源调度，降低响应时间和迭代速度，使得软件开发团队能够更快地适应市场变化和客户反馈。
3. **云计算环境**：利用云计算的优势，如虚拟化资源、自动化工具和动态扩展能力，来支持高效、可伸缩的资源管理。
4. **生命周期管理**：将资源调度融入软件开发生命周期，确保每个阶段都有适当的资源配置，从需求分析、设计、编码到测试，直至部署和维护。
5. **跨学科合作**：通过众包模式，鼓励开发者、客户和其他利益相关者之间的紧密合作，共同创造价值。
6. **规则与实践结合**：论文还可能详细阐述了如何制定和实施这些规则，以及如何通过案例研究或实验评估其效果。
总结来说，这篇研究论文为云计算环境下的敏捷软件开发提供了一种创新的资源调度策略，旨在通过优化用户参与、响应速度和跨领域协作，以提升整个软件开发过程的效率和灵活性。这对于企业应对快速变化的市场需求和保持竞争优势具有重要意义。
J. Cent. South Univ. (2016) 23: 2906−2916
2908
In condition that, each user sends a request to PRM
for resource
i
R
u
and the processing ratability of cloud
servers is supposed to be 100%, the following two cases
occur:
Case 1: Suppose that PRM allocates equal resource
items
)(
RRR
upp
to each user, which PRM needs
to develop the resource items p
R
and the development
cost denotes p
R
·m·p
p
. Thus, the PRM profit can be
represented as Eq. (1) and the user cost can be
represented as Eq. (2); Then, the idle resource items
denote
m
i
RR
i
up
1
)( and the resource utilization of
IRPs is 0.
Case 2: PRM allocates resource items p
R
(the
uncertain relation between p
R
and
i
R
u to each user). PRM
at least needs the resource items p
R
·m. And the
development cost at least reaches the amount of p
R
.
Suppose that there exist resources items k(k<=m)
satisfying the user needs in the existing RSDP, and then
the PRM profit should be represented as F(c)=k·u
p
·p
R
−
m·p
R
·p
p
(k≤m). The total user cost is k·u
p
·p
R
(k≤m) and
the idle resource items is represented as expressed as
m·p
R
−
k
i
R
i
u
1
and the resource utilization of IRPs is 0.
From above discussion, we find that the existing
RSDP, characterized as a centralized allocation
mechanism, assigns the equal resource for each user;
thus, this mode constrains the variability and scalability
of PRM process resource and results in a higher
development cost as well as a lower utilization of process
resource. On the other side, the user charges vary with
PRM price and users have no right to participate in
pricing. So, it is difficult to rent the appropriate process
resource according to user capabilities and expectations.
In this existing RSDP, PRMs ignore the process resource
usability of IRPs and make RSDP almost be in a lower
efficiency and a higher operating cost.
2.1 Foundation of model construction
We introduce the crowdsourcing cloud service to
agile software process and release resource needs by
crowdsourcing pattern. And then we provide software
resources service for team needs and external needs by
collecting the individual resource provider (IRP) from
outside the project team, which thus greatly reduces the
duties and pressure of project resource manager (PRM).
Meanwhile, we proposed the locality-aware resource
scheduling method for optimizing the resource
scheduling in crowdsourcing cloud service pattern
(CCSP) and improve the resource use efficiency in agile
software process. Finally, we reduce the overall resource
consumption and cost for the team, and promote software
process agility.
In our improved model, the centralized pattern of
resource allocation and scheduling has been broken for
sharing PRM responsibilities. In CCSP, PRMs, only as a
resource agent, provide leasing process resource for
individual applicants instead of providing storage
resources directly, and then allocate and schedule
individual storage resources dynamically according to
user expectations and IRPs constraints.
In KantoroVich-Hitchcock problem of operational
research, we find the similar basis that both models’
optimization can be reduced to multi-objective linear
optimization problems. Then this paper further proposes
the improved approach for allocating and scheduling user
storage resource, which finally maximizes the interests
of all parties in CCSP.
According to the mathematical principles of
Kantoro Vich-Hitchcock problems, the resources
scheduling process in CCSP can be described as follows:
PRM, as a resource agent, needs to distribute one kind of
resource provided by m sources (A
1
, A
2
, …, A
m
) to n
resource of IRPs (B
1
, B
2
, …, B
n
). During the allocation
process, users can provide the highest price uP
i
and
regard the price of IRPs as the cost
j
p
p in allocation
process. The balance sheet and price list of CCSP just
seem as that of Cantor los-marvel cork problems, shown
as in Table 1 and Table 2. Obviously, this problem may
be reduced as how PRMs allocate process resource of all
IRPs to maximize benefits under the premise to satisfy
user expectations.
Suppose that
ij
s
u denotes the number of the j-th
PRM resource items assigned to user i. There are three
types that the PRM allocates the IRPs’ resource to users.
The types are as followings: 1) The user sends a
request directly to the IRP. 2) According to the user′s
request parameters, PRM, as an agent, directly allocates
resource. 3) PRM, as an agent, uses formula (3) to
allocate the resource of IRPs.
Because all the IRPs’ parameter does not meet all
Table 1 Balance sheet of CCSP
Resource B
1
B
2
… B
n
User request
A
1
1
R
u
A
2
2
R
u
…
…
A
m
2
R
u
1
R
p
2
R
p
…
n
R
p
Table 2 Price list of CCSP
Resource A
1
A
2
… A
m
B
1
B
2
… B
n
A
1
1
R
u
2
R
u
m
R
u
1
p
p
2
p
p
n
p
p
A
2
1
p
p
2
p
p
n
p
p
…
…
…
…
A
m
1
p
p
2
p
p
n
p
p
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